Fall prevention interventions in primary care to reduce fractures and falls in people aged 70 years and over: the PreFIT three-arm cluster RCT

Article history

The research reported in this issue of the journal was funded by the HTA programme as project number 08/14/41. The contractual start date was in September 2010. The draft report began editorial review in June 2019 and was accepted for publication in January 2020. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.

Copyright statement

© Queen’s Printer and Controller of HMSO 2021. This work was produced by Bruce et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

2021 Queen’s Printer and Controller of HMSO

Background

Falls and fractures are a major public health burden. Falls can be associated with loss of independence and reduced functionality and are a major contributor to premature admission to nursing home or long-term care. 1,2 The risk of falling increases with age. Half of people aged ≥ 80 years fall at least once per year. 3 Most falls result in no injury, but can lead to fear of falling and loss of confidence in mobility. Falls can cause serious injury: each year, fracture and hospitalisation occurs in about 5% of community-dwelling older adults with a history of falling. 4 In 2016, there were 255,000 falls-related emergency hospital admissions in England among people aged ≥ 65 years. Demographic change means that the number and proportion of older people in the population is rising. In 2016 there were 1.6 million people in the UK aged ≥ 85 years (2% of total population), and this is projected to double to 3.2 million by 2036. 5 Fractures in older people will become increasingly common and this will have a major impact on use of health-care resources and service provision.

Costs of fractures and fall-related injury

The health and social care costs associated with fractures and fall-related injuries are high. Fractures and falls in those aged ≥ 65 years account for 4 million bed-days per year in England alone, at an estimated cost of £2B. 6 Direct health-care and associated social costs arise from the management of these injurious falls and fractures (the majority of costs arise from hip fracture). 7 Mortality is high in people who sustain a hip fracture: 10% die within 1 month and 30% die within 1 year of fracture. 8

Falls services in the UK NHS

Falls are a hallmark of age and becoming frail. 1 Falls have a multifactorial aetiology, with many risk factors, some of which are modifiable. Risk factors include impairments or instability of gait and balance, visual problems, cardiac rhythm abnormalities and syncope, polypharmacy and certain classes of ‘culprit’ or psychotropic medication, cognitive impairment, multiple comorbidity, foot disorders, and home and environmental hazards. Early clinical trials from the USA targeted the assessment and treatment of multiple risk factors and intervention strategies, termed multifactorial falls prevention (MFFP). These early trials were promising, indicating that multiple risk factor intervention strategies reduced risk of falling among community-dwelling older people. 9 These studies from the 1990s provided the basis for the mandatory establishment of secondary prevention in the UK:10 falls services providing MFFP interventions for people with a history of falling were subsequently introduced in England. 11 Multifactorial risk assessment, followed by targeted treatment of individual risk factors, is recommended for falls prevention in the UK, supported by clinical organisations [e.g. the American Geriatrics Society, the British Geriatrics Society and the National Institute for Health and Care Excellence (NICE)]. 2,3 These UK NHS services vary considerably in terms of service design, models of delivery and professional skill mix. 12,13

Evidence of effectiveness of falls prevention interventions

At the time of preparing the Prevention of Falls Injury Trial (PreFIT), numerous small trials had investigated the effectiveness of alternative falls prevention strategies. A 2012 Cochrane review14 (59 trials, n = 13,264) of falls prevention strategies found that exercise programmes, particularly those focused on strength and balance retraining, reduced rate of falls (i.e. number of falls) by 30% and risk of falling (i.e. number of people falling) by 18%. 14 Certain strength and balance interventions were found to be effective, especially those including targeted, individualised programmes progressed over time. However, adherence to exercise remained a challenge. Among the 59 falls prevention trials investigating exercise, only six had recorded fractures outcomes, totalling 45 fracture events.

The same Cochrane review14 identified 40 trials (n = 17,195) of MFFP studies. The review identified evidence for weaker effectiveness of MFFP interventions on falls outcomes, reporting that these interventions may reduce the rate of falls but not number of fallers (falls risk). 14,15 Among the 40 falls prevention trials investigating MFFP, only 11 had recorded fracture outcomes (totalling 289 events), but some trials included non-fracture injury. 14 Typically, the interventions identified by the Cochrane review were too poorly specified to be reproducible. These Cochrane reviews have since been updated; however, the overall conclusions are unchanged. 16 The reviews continue to highlight methodological deficiencies in existing trials, with many studies being underpowered and lacking robust data on important outcomes, including quality of life (QoL), fracture, costs of intervention and cost-effectiveness.

There is a need for robust evidence, with economic evaluation, to justify NHS provision of these falls prevention services.

Rationale for the PreFIT

Falls prevention services are widely implemented throughout the NHS, yet gaps remain in evidence regarding the prevention of falls. One of the main purposes of falls prevention is to reduce fractures and other serious injuries. 17 Adequately powered studies are required to investigate the effectiveness of such initiatives on clinical and patient-reported outcomes. This cluster randomised controlled trial (RCT) was designed to compare the effectiveness of alternative falls prevention strategies, using a screen-and-treat approach embedded within primary care, to investigate the prevention of falls and fractures in older adults.

The aim of the PreFIT was to determine the comparative clinical effectiveness and cost-effectiveness of three alternative primary care-led fall and fracture prevention strategies for older people living in the community: advice only; advice with screening for falls risk, with referral to an exercise programme for those at higher risk; and MFFP. Outcomes included fractures, falls, QoL, mortality and health-care resource use.

Research objectives

To estimate the clinical effectiveness and cost-effectiveness of the three alternative falls prevention interventions (advice only; advice supplemented with risk screening and referral to either exercise; and MFFP) in older adults. Our intention was to conduct a pragmatic trial and to include a process evaluation and a within-trial cost-effectiveness analysis.

Overview of the report

This report is structured across six chapters. We present methods (see Chapter 2), intervention development and description (see Chapter 3) and trial results (see Chapter 4) and describe the findings of the economic evaluation (see Chapter 5). Finally, we provide an overarching discussion and conclusion of our findings (see Chapter 6).

Trial design and setting

We conducted a three-arm, pragmatic, cluster RCT design, with a parallel economic analysis. The setting was English primary care. We have published a detailed description of the trial protocol elsewhere. 18

Eligibility criteria

Participant exclusions by general practitioner

After generation of the random sample lists, general practitioners screened lists to remove patients who should not be approached (if not already removed via electronic search criteria). Predetermined reasons included any illness with an end-of-life prognosis of < 6 months or residence in nursing or residential accommodation.

Postal invitation and participant consent

Practices posted an invitation pack containing a participant information sheet, baseline questionnaire and consent form. We sought consent for multiple levels of access to medical data, including medical records and routine data held by the NHS Health and Social Care Information Centre (HSCIC) (NHS Digital from April 2016). At the time of trial launch, the wording of consent forms was appropriate for access to HSCIC data and approved by the Research Ethics Committee and relevant monitoring committees. Ethics issues were considered in relation to the cluster trial design. 18

Allocation sequence generation and randomisation

The unit of cluster randomisation was the GP. Once we had recruited approximately 150 participants from each of the three GPs, or no further responses were being received, GPs were then randomised. To ensure allocation concealment, we did this in blocks of three (one allocated to each treatment arm). No stratification was used. Randomisation was based on a computer-generated randomisation algorithm held and controlled centrally in Warwick Clinical Trials Unit by an independent programmer. Trial administration staff members were informed of GP allocation by e-mail. Treatment allocation was coded and unavailable to the trial management team.

Blinding

We adhered to the Consolidated Standards of Reporting Trials (CONSORT) statement 2010 update extension for cluster randomised trials. 19 Owing to the nature of the interventions, it was not possible to blind therapists or services delivering exercise or MFFP. Senior members of the research team were blind to GP and treatment allocation for the duration of the trial. We undertook data cleaning and fracture adjudication of suspected and confirmed events blind to treatment allocation.

Trial interventions (advice, exercise and multifactorial falls prevention)

We describe trial interventions in Chapter 3 and in two intervention development papers. 20,21 In brief, we randomised practices to deliver one of three interventions: advice leaflet only; advice leaflet supplemented with screening for falls risk, followed by exercise; or MFFP. The Age UK (www.ageuk.org.uk; accessed 21 April 2020) Staying Steady booklet22 was used for the advice intervention. This was mailed out after practice randomisation. Those randomised to active interventions received the Age UK advice leaflet with the falls risk balance screener. The exercise intervention was based on the Otago exercise programme (OEP), targeting lower-limb strength, balance retraining and walking. 23 Practices randomised to intervention invited participants identified to be at high risk of falling to attend a 6-month PreFIT exercise programme or the MFFP programme (Table 1). The MFFP intervention comprised an individualised, 1-hour falls assessment with appropriate onward referral for treatment, including referral to the PreFIT exercise intervention if gait or balance risk factors were identified. We based the MFFP intervention on evidence-based guidelines for falls risk assessment and treatment pathways. 2,11

Screening and referral to active intervention

All participants received the advice booklet by post. Advice arm GPs delivered no further trial interventions. We used a primary care screening approach to determine onward referral of participants to the active interventions of exercise or MFFP. A short self-complete falls risk screening survey, based on previous research,24 was posted from, and returned to, GPs. Participants were categorised as being at risk of falling based on responses to falls and balance questions (high risk = multiple faller; intermediate risk = one fall or balance problems). These participants were offered the opportunity to attend for further assessment and treatment, either exercise or MFFP. Participants deemed to be at low risk (no history of falls or balance problems) received no further intervention other than the advice leaflet.

Co-interventions

No restrictions were placed regarding other agencies or health-care services contacting participants about fall prevention strategies. At trial closure, participants continued with usual health care and no further ancillary care was provided.

Baseline data: practices and participants

Descriptive data collected on GPs included practice-level deprivation at randomisation, using the UK National Index of Multiple Deprivation, which scores from 1 to 10 (from most deprived to least deprived). 25 Baseline descriptive data on participants included mobility, difficulties with mobility, cognition and activities of daily living (ADL). Mobility questions, adapted from population surveys of older adults,26,27 captured difficulties when balancing on a level surface, ability to walk outside the house, average time spent walking and difficulties with balance when performing common ADL (e.g. taking a bath, dressing). A clock-drawing test cognitive screener was included at baseline only. 28 Scoring was a 6-point system according to visuospatial aspects and the correct denotation of time: normal cognition (score 6), minor visuospatial errors (score 5), mild (score 4), moderate (score 3) or severe (score 2) visuospatial disorganisation of time or no reasonable representation of a clock (score 1).

Outcomes

Secondary outcomes

Secondary outcomes included falls, health-related quality of life (HRQoL), frailty, mortality and health-care resource use.

Data collection: postal questionnaires

Questionnaires were printed in large font, with a freephone number on the front page of the booklet and final free-text section for comments. Draft versions were modified after feedback from older people attending a community social group. We posted questionnaires with an explanatory cover letter. We collected core outcome data on fractures, falls, mobility, EQ-5D-3L and health-care resource by telephone if no response was received after one postal reminder. Any participant who reported a fracture event in the questionnaire was sent a separate questionnaire to elicit date, site of fracture (e.g. upper arm, wrist) and details of any hospital admission and/or radiographs taken. Participants reporting more than one fracture were contacted by telephone for further details.

Process evaluation

We measured a range of process evaluation indicators relating to intervention uptake and delivery, including time from randomisation to postal risk screener administration, uptake and predictive utility of the postal screener, uptake of active interventions, and duration and ‘dose’ of treatments delivered by exercise therapists or MFFP assessors.

The response rate to postal falls risk screeners was assessed for screening yield and utility by assessment of prediction of falls over 12 months, using area under the curve (AUC) and 95% confidence intervals (CIs), as per recommended guidance for evaluation of prediction models. 36 Intervention-related process measures for exercise included changes in strength and balance over time; thus, first and final assessments using the chair stand test (CST), the 4-test balance scale (4TBS), weights (kg) and repetitions were prescribed. We defined ‘work’ done as the product of the number of repetitions and amount of weight prescribed at each contact point (weights × repetitions). 37 We recorded the number of contacts and reported participants as having either fully or partially adhered to the exercise programme (fully adhered = six contacts or until discharge by therapist; partially adhered =  fewer than six contacts). We recorded the number of MFFP assessments among those invited to attend. The skill mix of assessors was recorded by region, and participant uptake of interventions was explored by region.

Data management

Questionnaires were scanned using FORMIC FUSION™ software (Formic Ltd, Staines, UK), which includes internal system validation checks. All scanned questionnaire entries were manually checked by the research office. All other data were manually entered by data clerks onto the bespoke database designed for the trial. All data were validated using range checks, outliers, missing data and date discrepancies. Anomalies were checked against original data sources for rectification.

Data analyses

Statistical analyses

Statistical analyses were carried out using StataSE® version 15 and 16 (StataCorp LP, College Station, TX, USA). All statistical tests were two sided and performed at the 5% significance level. We undertook three levels of analysis: (1) intention to treat (ITT), (2) a nested ITT on data from those at higher risk of falling and (3) complier-average causal effect (CACE) analyses. Unadjusted and adjusted estimates were obtained for all statistical regression models. Adjustment was made for the corresponding baseline variable (participant age, sex, falls history and GP deprivation score). The main ITT analysis included all randomised participants. In a nested ITT analysis, we compared treatments just among those at higher risk of falling, classified in accordance with the baseline questionnaire.

Treatment comparisons

The primary analysis compared all participants randomised to advice with all participants randomised to exercise and, separately, all participants randomised to advice with all participants randomised to MFFP. Only if there was evidence of a statistically or clinically important difference for either of these did we plan to proceed with comparing exercise with MFFP.

Descriptive analyses

Data were summarised and reported in accordance with the CONSORT guidelines for cluster RCTs. 19 Recruitment and participant flow are reported using CONSORT flow diagrams at the level of GP and participants in Chapter 4 (see Figures 1 and 2). Participant data from randomisation to 18 months were analysed by treatment arm and by risk classification using falls and balance questions from the baseline questionnaire. Sociodemographic variables were summarised by intervention arm, with mean (SD), median (range) and missingness reported for continuous variables, and number (%) for categorical variables. Unadjusted and adjusted estimates were obtained for all statistical regression models. Adjustment was made for the corresponding baseline variables: GP deprivation score, participant falls history, age and sex.

Secondary outcomes

Other secondary outcomes and baseline measures

Other clinical outcomes and baseline measures were summarised by treatment arm (SF-12, EQ-5D-3L, frailty, mobility and difficulties with ADL, cognitive impairment and self-reported health conditions). Mean values (SD), median (range) and missingness were reported for continuous variables, and number (%) for categorical variables. For secondary analyses, we estimated treatment effects by age, sex, falls history, frailty and cognition. Frailty status was fitted using the random-effect logistic regression model, unadjusted and adjusted, with the odds of being frail compared with non-frail by each active treatment arm compared with advice modelled. We followed validated scoring guidelines for all measures. Descriptive data for the cognition test were summarised as having higher compared with lower cognitive functioning. An additional secondary analysis was undertaken on fractures occurring after the 18-month follow-up period for the entire observation period for which HES data were available. For these events, confirmed fractures were based on only hospital statistics, as GP and self-report were not gathered beyond 18 months. We report events from the extended period as per events for the main trial.

Missing data

As fracture data were available for all participants from combinations of HES and GP records, we had no missingness for the primary outcome. We assessed missing data for the falls outcome. It was not possible to perform multiple imputation (MI) owing to skewness in the distribution of the data. Instead, we assessed missingness (no response) by looking at the proportion of missing data across treatment arms for the falls data within each time interval.

Adverse event reporting

A safety-reporting protocol for related and unexpected serious adverse events (SAEs) and directly attributable adverse events (AEs) was developed. An AE was defined as any untoward medical occurrence in a participant that did not necessarily have a causal relationship with treatment. All participants were aged ≥ 70 years; thus, we expected common chronic diseases associated with age (e.g. osteoarthritis and musculoskeletal conditions). It was expected that some participants would experience uncomfortable effects from exercising, such as muscle or joint discomfort. These effects were anticipated and, provided they were short-lived, were not reported as AEs. AEs were reported if they occurred during any contact time with the therapist or assessor delivering an intervention, during an intervention session or when undertaking prescribed exercise, either supervised or unsupervised. All AEs were reported to the trial team and chief investigator within the required timelines, in accordance with the PreFIT safety-reporting protocol. A SAE was an AE occurring as a direct consequence of treatment that resulted in death, threat to life, hospitalisation, disability or incapacity. Any event that required professional medical attention included, but was not restricted to, serious sprains, joint dislocation, falls or other injuries occurring as a direct consequence of the intervention. All events were reported to the trials unit immediately after the therapist became aware of them, within 24 hours for SAEs. The SAEs and AEs were recorded in the trial database. Any event related to trial interventions was referred to and reviewed by the Data Monitoring and Ethics Committee (DMEC).

Pilot phase and protocol revisions

A pilot phase was undertaken in one locality from September 2010 to March 2012. Key changes during the pilot phase included to increase postal mail-out from 300 to 400 people to increase yield from initial invitation. Other changes over time included an update to the primary outcome. The original trial protocol defined peripheral fracture as any fracture to bones in the appendicular skeleton, thus limbs and limb girdles as well as cranial and facial bones, but with exclusion of compression fractures in bones constituting the vertebral column (lumbar, thoracic and cervical vertebrae, sacrum and coccyx). This definition of appendicular skeleton also excludes the thoracic cage (sternum and ribs), as per the internationally agreed definition published by the ProFaNE network. 29 However, the definition of fracture events had evolved since protocol development. There was broad recognition among the clinical community that the ProFaNE consensus, published in 2005, required updating to reflect the contemporary epidemiology of trauma-related fractures in older people. 49 As populations age, the incidence of trauma-related skull, rib, vertebral and facial fractures increases. 50–53 During the pilot phase, we had developed methods for extracting accurate fracture data from both HES and GP records. It was apparent that fracture reporting was of sufficient quality to distinguish compression fractures of the vertebral column. We updated the PreFIT protocol and trial registry to include these fractures from HES and GP records when these were clearly consistent with a trauma mechanism. Therefore, when there was a clear description of trauma or fall, or when the fracture presentation was consistent with trauma and clearly mapped to the ICD-10, falls codes were generated from HES. Reports of vertebral osteoporotic compression fractures in GP records were excluded unless clearly linked to a report of trauma or fall.

Statistical models were revised from generalised estimating equations to generalised linear mixed-method models to account for data type and dispersion. These model revisions (updated from the original application) were reported in the prespecified analysis plan, approved by external committees and described in the published trial protocol. 18

Monitoring and approval

Regional and site-specific approvals were obtained from regional NHS research and development departments. The study was approved by the National Research Ethics Service (Research Ethics Committee reference 10/H0401/36, version 3.1, 21 May 2013), with approval granted by the Derbyshire Research Ethics Committee on 29 April 2010 (Table 2). Funder-led monitoring meetings to review pilot study recruitment and intervention data were held on 4 April 2012 and 14 September 2012, and written approval to proceed to the main trial was received in October 2012. Of the amendments approved by the Research Ethics Committee, seven were substantial and one was non-substantial.

Patient and public involvement

User groups and patient public representatives were involved in the design of materials and implementation of the trial. Older people attending a community support group and social lunch group in the Warwickshire region were invited to review patient-facing materials, including questionnaires, balance screening, cover letters and patient information sheets. The Trial Steering Committee (TSC) included an independent lay member. A patient dissemination event, attended by 48 participants and their partners or carers, was held at a University of Warwick conference centre on completion of the trial.

Trial Steering Committee

The TSC was responsible for monitoring and supervision of trial progress.

Data Monitoring and Ethics Committee

The independent DMEC monitored ethics, safety and data integrity aspects of the trial. Pilot data were reviewed by the TSC and DMEC; we proceeded to the main trial after approval by the DMEC, TSC and funder.

Introduction

This chapter presents a description of the trial interventions; sections are based on published work describing the exercise and MFFP interventions. 20,21 Intervention development was undertaken using Medical Research Council guidance54 for the development of complex interventions and following methodology used in other rehabilitation trials supported by the National Institute for Health Research Health Technology Assessment programme. 55,56 A key consideration was the selection of interventions suitable for testing and implementation in the UK primary care setting.

Advice intervention

A scoping survey of UK falls services, referral pathways and information materials for older people had been completed before we started the PreFIT. 12 Falls prevention leaflets for older adults were reviewed for content and clarity. The Age UK’s Staying Steady booklet22 was selected for the advice intervention because of the positive emphasis on remaining steady and physically active. The Age UK leaflet contained useful advice about improving strength and balance rather than focusing on the consequences of falling. This 29-page colourful booklet provides clear information about eyesight, hearing, foot care, managing medications, checking the home environment for falls risks and dealing with anxiety about falling. It contains practical advice on seeking help from the NHS, contact details and telephone numbers for other organisations for older people and links to further reading. The leaflet was provided free of charge by Age UK. All trial participants received this booklet by post after the GP was randomised. Those randomised to the advice arm received a booklet with a covering letter only, then no further planned intervention.

Rationale and scientific principles for the exercise intervention

Content of the PreFIT exercise intervention

Procedures for delivery of the PreFIT exercise intervention

Exercise adherence

Adherence to exercise can be challenging, especially over long periods. 66 Therapists were trained to encourage participants to be actively involved in the decision-making process regarding exercise selection and goal-setting. The positive outcomes associated with exercise and walking were emphasised, such as maintaining independence and remaining active, rather than reduction or prevention of falls. 86 Calendars or diaries can improve adherence to exercise. 87 These were provided in participant folders to serve as a reminder to exercise and as a prompt to self-monitor behaviour. Diaries were reviewed at follow-up appointments and therapists provided positive and constructive feedback. 88 Additional behaviour change techniques included action planning, identification of barriers to exercise and use of problem-solving strategies, including SMART (specific, measurable, achievable, relevant and timely) goal-setting to motivate participants to continue exercising. 88

Data collection

Therapists completed a detailed exercise treatment log for each participant. At the final assessment, participants and therapists were asked to report whether or not they felt that they had improved in strength, balance and walking. Complete copies of anonymised treatment logs were returned to the study office for data entry and analysis.

Exercise quality control assessments

A research physiotherapist (SF) visited intervention sites to conduct quality control assessments. Every therapist responsible for intervention delivery was visited at least once. These visits were arranged shortly after completion of training and after the therapist had undertaken one or more assessments of trial participants. The aim was to ensure that the intervention was delivered in a standardised manner, in accordance with the trial protocol and therapist training. The quality control assessor considered therapist competency in all aspects of intervention delivery. At least one appointment, either the baseline or a follow-up contact, was observed by the quality control assessor. We used a standardised checklist to monitor exercise prescription and effective and safe delivery of the programme, including progression and adherence to the protocol. Checks were made of all trial-related documentation, including appointment spreadsheets, prescription logs, treatment forms, withdrawal forms and procedures for AE reporting. Each therapist received a written, graded report (satisfactory, minor concerns or serious concerns) and follow-up visits were arranged if necessary. The research physiotherapist provided regular clinical supervision and support to therapists throughout the duration of the trial. A password-protected online forum was developed for all therapists to post questions or comments and share experiences about the trial.

Multifactorial falls prevention intervention

Content of PreFIT MFFP assessment

Sections of text in this chapter have been published in an open access journal and adhere to the Creative Commons licence for open access articles. 20 See Report Supplementary Material 1 for an overview of MFFP.

Culprit medications

The original Tinetti et al. 9 programme, from 1994, highlighted specific classes of high-risk or ‘culprit’ medications. These included antihypertensives, antiarrhythmics, anticonvulsants and antidepressants among many others. Others96 have since defined the main culprit medications contributing to risk of falls as those targeting the central nervous system. Other classes of drugs, although the evidence base for the causal relationship with falls is weaker, include urinary anticholinergics and alpha-blockers. 96,97 In 2010, the National Audit of Falls and Bone Health in Older People specifically highlighted psychotropic medication and night sedation as potential causes of falling. 6 We considered the available evidence to inform the format of the PreFIT medication review. We used the national audit6 and the UK Department of Health and Social Care (DHSC) policy as guidance. 94 This policy document described different levels of review (from level 0 to level 3), relating to intensity of review, skill of assessor and whether or not the review was conducted in the presence of the patient.

PreFIT medication reviews

In the PreFIT, two levels of medication reviews were carried out: first, the assessor screened all drugs prescribed to every participant attending a MFFP assessment. This involved a face-to-face discussion about prescribed drugs and use of over-the-counter drugs during the MFFP assessment, as per DHSC level-1 review. This initial screen was for high-risk medications based on our own PreFIT classification and listing of (1) psychotropics and (2) other culprit medications. Psychotropic medications included any antidepressant, antipsychotic, sedative or anxiolytic, or mood stabiliser drugs. Other culprit medications included antihypertensives, antiarrhythmics, diuretics, vestibular suppressants, analgesics, anticonvulsants, anti-Parkinson drugs and vasodilators. Any participant prescribed one or more of these drugs was then referred for a more detailed review, that is, a general practitioner-led clinical medication review, corresponding to a level-3 review. 94 This involves a separate appointment between only the participant and general practitioner, either face to face or by telephone if the medication revision is considered minor. One or more nominated general practitioners from each practice were given training on high-risk medications in older adults, risks of polypharmacy and how to conduct a falls-related medication review. A consultant geriatrician or specialist registrar, supported by a member of the trial team, delivered training to general practitioners. Every GP randomised to MFFP intervention received medication review training.

Factors not included in the PreFIT multifactorial falls prevention assessment

Detailed tests of urinary incontinence, hearing, osteoporosis risk and comprehensive assessment of neurological and cardiac systems are not part of the MFFP package. The falls interview included screening questions on urinary incontinence in relation to any fall or near-miss event. At the time of development, none of the published clinical trials investigating MFFP included screening of osteoporotic risk, although in many cases the description of intervention content was inadequate. 15 We purposefully excluded the addition of a protocol that included provision of bisphosphonates and bone medications to avoid the possibility of interpreting MFFP as effective when medications could have been responsible for any observed effect. We were cognisant of the different clinical backgrounds of assessors and barriers to accessing trained medical practitioners in some settings. Safety was also a consideration; for example, we did not ask assessors to undertake carotid artery stimulation to check for carotid sinus hypersensitivity.

Staff training

Health-care staff responsible for delivering the MFFP intervention received 4–5 hours of structured training, either in the GP or in hospital. Health-care staff members undertaking falls assessments ranged from experienced falls team personnel (consultant geriatricians, falls nurses, occupational therapists or physiotherapists) to GP or research staff members (e.g. advanced nurse practitioners, practice nurses and/or research nurses). Staff members were required to have a nursing or allied health-care background with professional registration. Training in medication reviews was given either to one nominated lead general practitioner or to all general practitioners in each GP randomised to MFFP as a scheduled educational session. A geriatrician with extensive experience in falls assessment (RS, KW, SR or JT) and senior researcher from the trial team jointly provided training (JB). Each assessor received a detailed intervention reference manual before training, which described risk factors, treatment pathways, trial procedures, flow charts for onward referral, etc. Laminated prompt charts and easy-to-use instruction sheets were provided (e.g. for medication assessments).

Recommended treatments

Assessors provided verbal and written advice to trial participants and also arranged onward referrals to consultant-led falls services, physiotherapy, GPs, occupational therapy, social services, etc., as per the treatment pathways we recommended for each risk factor and as per detailed manuals and the published intervention paper. 20 Trial intervention manuals will be available from the University of Warwick repository (http://wrap.warwick.ac.uk/85689/; accessed 7 October 2020).

Data collection

Assessors completed a MFFP risk assessment form for every participant randomised to MFFP. All risk factors and test findings were recorded. For onward actions or referrals, the date of referral was recorded, as was the name of person or doctor referred to. Completed copies of falls risk assessment forms with study identification number (name redacted) were returned to the study office for data entry and analysis.

Multifactorial falls prevention quality control assessments

Each trained assessor was observed while carrying out a falls assessment (with verbal permission from the participant) to ensure compliance with the intervention procedures. These quality control visits were carried out, after the assessor had been trained and carried out one or more MFFP assessments, by trial staff members (JB or SF) or by a consultant geriatrician (RS) or, in the Devon region, by a medical registrar with expertise in falls assessments (Lindsey Rohan or Meera Sritharan). A 37-item standardised checklist was developed and used to ensure that all aspects of the MFFP assessment were reviewed during each quality control visit. No input was given during the quality control assessment, unless the assessor queried a specific issue. A written, signed, graded (satisfactory, minor concerns or serious concerns) report was given to assessors, with a follow-up visit arranged if necessary. The research team provided regular supervision and support to therapists throughout the duration of the trial. Experienced medical staff members were available to deal with any complex clinical queries (KW and RS).

Study timeline

Trial recruitment started in September 2010, with a pilot phase conducted in 12 GPs in Devon to refine procedures. Recruitment paused from March to September 2012, while pilot data were reviewed by the funder, and restarted in September 2012. The final GPs were randomised in June 2014 and postal follow-up finished in 2016. NHS Digital provided final HES data sets including data up to the end of March 2016 in 2018. Data from all phases of the trial were combined for the analysis because there were minimal changes after the pilot.

Cluster (general practice)-level data

Information and advertisements about the trial were widely distributed via primary care newsletters and regional primary care research events. We received expressions of interest from 82 GPs. Sixty-three GPs were randomised from six localities across England: Birmingham and the Black Country (n = 2), Cambridgeshire (n = 6), Devon (n = 18), Warwickshire and Herefordshire (n = 12), Newcastle upon Tyne (n = 11) and Worcestershire (n = 14). We exceeded our GP recruitment target because we had three GPs already prepared for study entry at the time we randomised our 60th GP. The final triad of GPs was randomised and split across two different regions (Newcastle upon Tyne, and Birmingham and the Black Country). No GPs withdrew from the study. One GP closed down after recruitment and completion of intervention delivery, and the participants were registered with a new GP. This did not affect participant follow-up. We were, however, unable to obtain GP records for participants from this surgery. Figure 1 presents the cluster CONSORT flow chart for GPs. 19

FIGURE 1.

Cluster CONSORT flow diagram by GPs. a, Fall risk screener and attended treatment.

Sociodemographic characteristics of recruited general practices

Of the 63 GPs, 13 (21%) were in areas of social deprivation (score 1–3), 22 (35%) were in affluent areas (score 8–10) and the remainder were categorised as moderate [n = 28 (44%); score 4–7].

Participant recruitment and allocation

Participant-level flow is presented in the CONSORT diagram for participants (Figure 2). We invited 29,010 people to take part in the trial (see Appendix 1, Table 31). The mean number of people invited per GP was 387 (range 170–608). Out of the 29,010 participants, 9819 (33.8%) gave consent and returned baseline questionnaires. Nine people withdrew and seven died after providing consent and prior to GP randomisation. We randomised 9803 out of 29,010 participants (33.8% of those invited). The mean number of participants randomised per GP was 156 (range 115–201). Trial arms were well balanced for size, with 3223 (32.9%), 3279 (33.4%) and 3301 (33.7%) out of 9803 participants being allocated to advice, exercise and MFFP, respectively (see Appendix 1, Table 32).

FIGURE 2.

A CONSORT flow diagram by participants. a, Fall risk screener and attended treatment.

Completeness of primary outcome data

Useable fracture data were available for all participants for the whole study period. We obtained HES data for 9802 out of 9803 participants, with only one participant not providing permission for access. No participants withdrew consent for access to HES data after randomisation. Participant data were missing from one GP randomised to the exercise arm (159/9803, 1.6%), although HES data were available. There were 458 fractures confirmed by the adjudication panel during the 18-month follow-up period. Fractures were identified in both HES and GP records for 279 out of 458 (60.9%) fractures, only in HES records for 88 out of 458 (12.2%) fractures and only in GP records for 91 out of 458 (19.9%) fractures. Therefore, for 458 fracture events, source of fracture confirmation was from HES data for 367 (80.1%) participants and from GP records for the remaining 91 (19.9%) participants. The level of agreement (kappa statistic) between HES and GP records was 0.75 (95% CI 0.71 to 0.78; p < 0.001).

Completeness of secondary outcome data

Over the 18-month follow-up period, 1213 (12.4%) participants withdrew from the trial and 289 (2.9%) died. Response rates to postal questionnaires were good: 9064 (92.5%), 8578 (87.5%), 8136 (83.0%) and 7490 (76.4%) at 4, 8, 12 and 18 months’ follow-up, respectively (see Appendix 1, Table 32). There were no between-group differences in the numbers or characteristics of withdrawals and dropouts by treatment arm over the 18-month follow-up period. Participants who withdrew were more likely to be female, older, frailer and have a history of falling and poorer physical and mental health than those who remained in the trial. The characteristics of the randomised and analysed sample were very similar at baseline. Missingness in reporting of falls outcomes increased as the study progressed, similar to response rates. Missingness of falls outcome data was 10.0% at 4 months, 13.9% at 8 months, 18.1% at 12 months and 24.6% at 18 months.

Baseline characteristics of trial participants

Overall, our three groups were well balanced at baseline (Table 4). Participants’ mean age was 78 years (range 70–101 years) and just over half were female (5150/9803, 52.5%). The majority of participants were white and either married or cohabiting, and one-third lived alone. The mean age at leaving full-time education was 17 years. Sociodemographic characteristics were well balanced across treatment arms. Most participants were able to go outside unaided, although 20% (1913/9803) required a stick or support. Overall, participants were moderately active, with three-quarters self-reporting that they walked ≥ 1 hour every day. The majority of participants had no cognition impairment, although 9% (870/9803) had lower scores (0–4) on the clock-drawing test, indicating a degree of cognitive impairment.

One-quarter of the sample selected no comorbidity from the list of common conditions, over half selected one or two conditions and 20% reported having three or more medical conditions diagnosed by a doctor (see Table 4). The most common self-reported comorbidity was arthritis, with almost half reporting rheumatoid arthritis or osteoarthritis (4403/9803, 44.9%). Heart troubles or angina (2679/9803, 27.5%) and diabetes (1403/9803, 14.3%) were also common. The prevalence of frailty was 21% (2005/9803). HRQoL scores indicated good levels of physical health-related and mental health-related QoL on both the SF-12 and EQ-5D-3L measures (see Table 4). Pain and discomfort was common, with 60% reporting moderate or severe pain. One-fifth of respondents reported moderate or severe anxiety and depression. The most frequently reported ADL difficulty was taking a bath, with 15% reporting a lot of difficulty or being unable to do. Most participants (> 90%) reported no difficulty or only a little difficulty performing other ADL.

History of falls and fractures in previous year

Out of 9803 people randomised, 3150 (32.1%) had fallen at least once in the previous year (see Table 4) and 4291 (43.8%) were deemed at higher risk of falling based on responses to the questions in the baseline questionnaire. A total of 324 (3.3%) participants reported having suffered a fall-related fracture in the year prior to recruitment. Although the proportions of participants falling, sustaining a fracture and being at risk of falling were similar across all three treatment groups on entry to the trial, participants in the MFFP arm reported a non-statistically significant higher falls rate in the previous year than those in the advice and exercise arms (87.3 vs. 70.6 and 73.5 falls per 100 years, respectively). This was due to five extreme fallers and, when extreme observations were removed, falls rates were very similar by treatment arm.

Primary outcome: fractures

We had a total mean follow-up period of 18.4 (SD 0.3) months, with a total of 14,853 person-years of follow-up data. There were no differences by treatment arm. The number of participants sustaining one or more fractures over 18 months’ follow-up was 379 out of 9803 (3.9%) (Tables 5 and 6). The total number of fractures by treatment arm was 133, 152, and 173 in those randomised to advice, exercise and MFFP, respectively. The unadjusted fracture rates were 2.76, 3.06 and 3.50 per person per 100 years in those randomised to advice, exercise and MFFP, respectively (see Table 5).

Other fracture analyses

We found no differences in time to first fracture between the exercise arm and the advice arm [hazard ratio (HR) 1.12, 95% CI 0.87 to 1.45; p = 0.38]. Time to first fracture was shorter for those randomised to MFFP than for those randomised to advice, but the difference was of borderline statistical significance (adjusted Cox regression model HR 1.28, 95% CI 0.99 to 1.63; p = 0.055). The Kaplan–Meier survival curve for time to first fracture, among all participants, by treatment group is shown in Figure 3. We found no difference in number of people with hip or wrist fractures by treatment arm (see Table 5). A total of 87 participants sustained a hip fracture over the 18-month follow-up period and, of these, one participant in the exercise arm sustained more than one hip fracture. Seventy-seven participants sustained a wrist or forearm fracture and, of these, three participants had more than one fracture (exercise, n = 1; MFFP, n = 2).

FIGURE 3.

Kaplan–Meier curve for time to first fracture over 18 months (n = 9803).

Secondary outcomes

Falls reported in questionnaires

We found no differences in falls rates over the 18-month follow-up by treatment group (Table 9). However, the falls rate was lower over months 4–8 in those randomised to exercise than in those randomised advice (adjusted RaR 0.78, 95% CI 0.64 to 0.96; p < 0.001). No differences were observed in fall RaRs at other interim time points, from randomisation to 4 months, months 8–12 months or months 12–18 (see Tables 9 and 10). Falls distribution by treatment arm over time is shown in Figure 4. The characteristics of participants who provided falls outcomes, that is, who returned questionnaires at 18 months, were also considered (Table 11).

TABLE 9 - Falls outcomes by treatment arm by time period

Adjusted for age, sex, GP deprivation score and log of baseline falls count in standard negative binomial regression.

Notes

Variable follow-up time is used as an offset in the model. Model fit = LR test of alpha = 0 (LR test vs. standard Poisson regression).

Analysis	Advice	Exercise	MFFP	Total
Randomised, n	3223	3279	3301	9803
One or more falls over 18 months, n (%)	1276 (39.6)	1277 (38.9)	1301 (39.4)	3854 (39.3)
Two or more falls over 18 months, n (%)	715 (22.2)	687 (21.0)	743 (22.5)	2145 (21.9)
Fallers, n (%)
0–4 months	586 (18.2)	605 (18.5)	605 (18.3)	1796 (18.3)
4–8 months	539 (16.7)	458 (14.0)	555 (16.8)	1552 (15.8)
8–12 months	514 (16.0)	500 (15.3)	502 (15.2)	1516 (15.5)
12–18 months	455 (14.1)	450 (13.7)	470 (14.3)	1375 (14.0)
Unadjusted fall rate over 18 months per person per 100 years (95% CI)	114.7 (113.8 to 115.7)	108.0 (107.3 to 109.0)	126.2 (125.4 to 127.1)	116.4 (115.8 to 116.9)
Adjusteda falls over 18 months per person per 100 years (95% CI)	105.6 (100.8 to 110.4)	104.4 (98.4 to 110.4)	127.2 (117.6 to 136.8)	112.8 (108.0 to 116.4)

FIGURE 4.

Plot of distribution of falls by treatment group over time (a) advice; (b) exercise; and (c) MFFP.

TABLE 10 - Unadjusted and adjusteda RaRs of falls by time period for each treatment comparison

Adjusted for age, sex, GP deprivation score and log of baseline falls count in random-effects negative binomial regression. Variable follow-up time was used as an offset in the model and sources of random effects are GPs. Model fit = LR test vs. standard negative binomial regression.

Results from standard negative binomial regression as LR test of random-effects negative regression model are not significant.

Time period	Exercise vs. advice		MFFP vs. advice
	RaR (95 CI%)	p-value	RaR (95% CI)	p-value
Over 18 months
Unadjusted RaR	0.96 (0.80 to 1.16)	0.69	1.12 (0.93 to 1.34)	0.22
Adjusted RaR	0.99 (0.86 to 1.14)	0.91	1.13 (0.98 to 1.30)	0.10
0–4 months
Unadjusted RaR	1.05 (0.79 to 1.39)	0.74	1.20 (0.91 to 1.58)	0.21
Adjusted RaR	1.12 (0.87 to 1.45)	0.47	1.19 (0.92 to 1.53)	0.18
4–8 months
Unadjusted RaR	0.77 (0.61 to 0.98)	0.03	1.11 (0.88 to 1.41)	0.37
Adjusted RaR	0.78 (0.64 to 0.96)	0.02	1.13 (0.93 to 1.37)	0.24
8–12 months
Unadjusted RaR	1.05 (0.86 to 1.28)	0.63	1.00 (0.82 to 1.22)	0.12
Adjusted RaRb	1.04 (0.91 to 1.21)	0.61	0.97 (0.84 to 1.13)	0.74
12–18 months
Unadjusted RaR	1.11 (0.88 to 1.40)	0.37	1.14 (0.90 to 1.43)	0.28
Adjusted RaR	1.10 (0.91 to 1.34)	0.33	1.13 (0.93 to 1.38)	0.21

TABLE 11 - Characteristics of analysed sample providing falls data at 18 months (n = 7490)

BMI, body mass index; FTE, full-time education.

Characteristic	Advice (N = 2493)	Exercise (N = 2500)	MFFP (N = 2497)	Total (N = 7490)
Age (years), mean (SD)	77.5 (5.5)	77.6 (5.5)	77.3 (5.4)	77.5 (5.5)
Age (years), range	70–101	70–96	70–98	70–101
Age band (years), n (%)
70–79	1721 (69.0)	1739 (69.5)	1777 (71.17)	5237 (69.9)
80–89	716 (28.7)	994 (27.8)	664 (26.6)	2074 (27.7)
≥ 90	56 (2.3)	67 (2.7)	56 (2.2)	179 (2.4)
Female, n (%)	1293 (51.9)	1286 (51.4)	1303 (52.2)	3882 (51.8)
Ethnicity, n (%)
White	2455 (98.5)	2456 (98.2)	2456 (98.4)	7367 (98.4)
Other	20 (0.8)	22 (0.9)	22 (0.9)	64 (0.8)
Missing	18 (0.7)	22 (0.9)	19 (0.8)	59 (0.8)
Marital status, n (%)
Married/cohabiting	1622 (65.1)	1593 (63.7)	1607 (64.4)	4822 (64.4)
Widowed	647 (25.9)	632 (25.3)	599 (24.0)	1878 (25.0)
Divorced/separated	120 (4.8)	176 (7.0)	159 (6.4)	455 (6.1)
Single	97 (3.9)	88 (3.5)	121 (4.8)	306 (4.1)
Missing	7 (0.3)	11 (0.5)	11 (0.4)	29 (0.4)
Living arrangement, n (%)
Live alone	785 (31.5)	800 (32.0)	779 (31.2)	2364 (31.6)
Live with others	1695 (68.0)	1684 (67.4)	1708 (68.4)	5087 (67.9)
Missing	13 (0.5)	16 (0.6)	10 (0.4)	39 (0.5)
Age left FTE (years), mean (SD)	17.0 (4.7)	16.7 (4.1)	17.0 (4.8)	16.9 (4.4)
BMI (kg/m2), mean (SD)	26.4 (4.7)	26.4 (4.4)	26.4 (4.4)	26.4 (4.5)
Balance difficulties walking on level, n (%)
Never/sometimes	2312 (92.7)	2341 (93.6)	2333 (93.4)	6986 (93.3)
Often/very often/always	169 (6.8)	147 (5.9)	156 (6.3)	472 (6.3)
Missing	12 (0.5)	12 (0.5)	8 (0.3)	32 (0.4)
Able to get outside, n (%)
Unaided	2078 (83.3)	2076 (83.0)	2108 (84.4)	6262 (83.6)
With stick/support or help only	396 (15.9)	409 (16.4)	378 (15.1)	1183 (15.8)
Cannot get outside at all	10 (0.4)	3 (0.1)	4 (0.2)	17 (0.2)
Missing	9 (0.4)	12 (0.5)	7 (0.3)	28 (0.4)
On average, hours/day walking
< 1	583 (23.4)	590 (23.6)	613 (24.6)	1786 (23.9)
1–2	936 (37.5)	959 (38.4)	910 (36.4)	2805 (37.4)
> 2	960 (38.5)	938 (37.5)	966 (38.7)	2864 (38.2)
Missing	14 (0.6)	13 (0.5)	8 (0.3)	35 (0.5)
HRQoL (EQ-5D-3L), mean score (SD)	0.79 (0.22)	0.80 (0.21)	0.79 (0.21)	0.80 (0.21)
Missing	88	102	108	298
HRQoL (SF-12 PCS), mean score (SD)	51.0 (9.9)	51.5 (9.8)	51.0 (10.1)	51.1 (10.0)
Missing	217	199	207	623
HRQoL (SF-12 MCS), mean score (SD)	50.7 (8.8)	51.0 (8.4)	51.0 (8.7)	50.9 (8.6)
Missing	217	199	207	623
Clock-drawing test score, n (%)
0–4	170 (6.8)	163 (6.5)	162 (6.5)	495 (6.6)
5–6	2294 (92.0)	2306 (92.2)	2283 (91.4)	6883 (91.9)
Missing	29 (1.2)	31 (1.3)	52 (2.1)	112 (1.5)
Frailty, n (%)
Frail	444 (17.8)	398 (15.9)	484 (19.4)	1326 (17.7)
Non-frail	2024 (81.2)	2067 (82.7)	1988 (79.6)	6079 (81.2)
Missing	25 (1.0)	35 (1.4)	25 (1.0)	85 (1.1)
Comorbidities, n (%)
None	600 (24.1)	613 (24.5)	620 (24.8)	1833 (24.5)
One or two	1229 (49.3)	1239 (49.6)	1226 (49.1)	3694 (49.3)
Three or more	664 (26.6)	648 (25.9)	651 (26.1)	1963 (26.2)
Fallen in previous year, n (%)
Yes	764 (30.6)	741 (29.6)	805 (32.2)	2310 (30.8)
No	1715 (68.8)	1743 (69.7)	1680 (67.3)	5138 (68.6)
Missing	14 (0.6)	16 (0.7)	12 (0.5)	42 (0.6)
Participants with fall-related fracture in previous year, self-report, n (%)	31 (1.2)	31 (1.2)	26 (1.0)	88 (1.2)
Participants at higher risk of falling, baseline questionnaire, n (%)	1028 (41.2)	1019 (40.8)	1063 (42.6)	3110 (41.5)
Missing	1 (0.04)	5 (0.2)	1 (0.04)	8 (0.1)

Falls reporting by diary card

Diary cards were sent to 9375 out of 9803 participants (95.6%) due to receive them [428 participants (4.4%) had either withdrawn or died before their allocated time period; Figure 5], of whom 7762 (82.8%) returned one or more completed diaries. Diary card response rate dropped slightly over each subsequent 4-month time period [randomisation to 4 months, 2758/3256 (84.7%); months 5–8, 2539/3093 (82.1%); months 9–12, 2465/3026 (81.5%)] (Table 12). Diary card non-responders were older and had poorer HRQoL scores on recruitment to the trial. Among the 6418 participants reporting falls in both data sources (diary card and questionnaire), there was substantial agreement in reporting (Cohen’s unweighted kappa test statistic 0.638). When there was lack of agreement, falls reporting was higher in diary cards than in the questionnaires. A separate manuscript reported on patterns of return (Table 13) and differences by data collection method. 115 In brief, we found an average 32% difference in the falls rates between the prospective diary cards and retrospective reporting. 115

FIGURE 5.

Flow chart for diary card data for prospective falls reporting.

TABLE 12 - Diary card response rate by time period and intervention

Row percentage.

Time period (months)	Participants returning diary cards, n (%)a
	Advice	Exercise	MFFP	Total
0–4	917 (33.2)	926 (33.6)	915 (33.2)	2758
5–8	847 (33.4)	849 (33.4)	843 (33.2)	2539
9–12	828 (33.6)	819 (33.2)	818 (33.2)	2465
All	2592	2594	2576	7762

TABLE 13 - Pattern of diary card response rate by time period

Row percentage.

Time period (months)	Diaries returned, n (%)a				Total
	One	Two	Three	Four
0–4	62 (2.3)	97 (3.5)	284 (10.3)	2315 (83.9)	2758
5–8	64 (2.5)	91 (3.6)	319 (12.7)	2065 (81.2)	2539
9–12	49 (2.0)	64 (2.6)	224 (9.1)	2128 (86.3)	2465
All	175	252	827	6508	7762

Subgroup analyses

We undertook subgroup analyses as per the prespecified analysis plan, that is, treatment effects were compared by age, sex, history of falling, frailty and cognitive impairment (Table 14). No statistically significant interaction effects were found in the RaR of falls by baseline participant characteristics.

Health-related quality of life over time by intervention

We assessed HRQoL over time by treatment group. Table 15 presents mean (SD) SF-12 scores and missingness for PCS and MCS scores. Findings for EQ-5D-3L scores are presented in Chapter 6. Differences in mean SF-12 scores were found at interim time points; however, no differences were found in overall mean SF-12 change scores between the advice and exercise groups or between the advice and MFFP groups over time (Table 16).

Frailty

The proportion of responding participants classified as frail was slightly higher at 18 months (1672/7490, 22.3%) than at baseline (2005/9803, 20.5%) (Table 17). There were no differences in the odds of being frail at follow-up by treatment comparison (Table 18).

Serious adverse events

No SAEs directly related to the interventions were reported. One participant sustained a fractured neck of femur during a trial procedure not related to the intervention [a fall sustained when returning from posting a follow-up questionnaire (exercise arm)]. Other AEs included a fall by one participant during MFFP assessment. This participant was a frequent faller and had been investigated by consultant-led services, with no cause identified. Another participant with a known history of angina reported experiencing angina when exercising at home (exercise arm). Both participants continued with interventions.

Process evaluation

This section presents overall key findings for screening utility and intervention uptake for the active interventions, that is, for those referred to exercise and MFFP and also for those in MFFP who were referred to exercise therapy. Findings are then presented in more detail for each intervention arm.

Time from randomisation to start of treatment

Median time from randomisation to the first exercise assessment was 14 weeks [interquartile range (IQR) 10–22 weeks]. This varied by region, from a median of 9 weeks in Birmingham and the Black Country to a median of 21 weeks in Devon. This was due to staffing issues within services; in some areas staff members were trained but then changed duties or departments at short notice. In the MFFP group, median time from randomisation to assessment was 16 weeks (IQR 13–23 weeks), ranging from 13 weeks in Newcastle upon Tyne to 25 weeks in Worcestershire. Delays in assessments were due to lack of availability of GP nurses to deliver assessments.

Exercise intervention

Adherence

Among the 697 participants attending exercise therapy, the overall median time spent in the programme was 25 weeks (IQR 16–27 weeks). Among the 454 [out of a total of 697 (65%)] participants who fully adhered to the recommended exercise programme, defined as having six or more contacts with the therapist, median duration in the intervention was 27 weeks (IQR 25–28 weeks) (Table 20). One-third of participants partially adhered to the intervention, defined as having fewer than six contacts with the therapist (243/697, 35%). There were no differences in adherence by region. There were no differences in adherence status by age or sex of participants.

TABLE 20 - Duration (weeks) spent in exercise intervention

MFFP and exercise = referrals to exercise programme based on MFFP risk assessment.

Treatment group	Time (weeks) spent in exercise intervention
	Participants, n (%)	Median (IQR)
Exercise (N = 697)
Partially adhered (fewer than six contacts)	243 (34.9)	10 (3–18)
Adhered (six or more contacts)	454 (65.1)	27 (25–28)
MFFP and exercisea (N = 203)
Partially adhered (fewer than six contacts)	79 (38.9)	12 (4–17)
Adhered (six or more contacts)	124 (61.1)	26 (25–30)

Contacts with therapists

The 58 trained therapists had a total of 3842 contacts with 697 participants. Over half of the contacts were face to face (2078/3842, 54%) and the remainder were by telephone (1764/3842, 46%). Although up to six contacts were recommended, 295 out of 697 (42%) participants had seven or more therapy contacts (Figure 6). Participants who fully adhered to the exercise programme had a mean of six contacts (SD 1.3 contacts) with the therapist, and those who partially adhered had a mean of four contacts (SD 2 contacts). There were no differences in mean number of contacts by region.

FIGURE 6.

Number of contacts with therapists for participants who attended the exercise intervention (n = 697).

Change in leg strength over time

Among those randomised to the exercise intervention, we measured change in performance in the CST and 4TBS over time. Final test data were available for those attending the final therapy session, either session 6 or session 7. There was a statistically significant improvement in performance over time: the mean CST score was 3.5 (SD 1.1) before intervention compared with 3.7 (SD 0.9) post intervention [mean difference (MD) 0.15, 95% CI 0.08 to 0.23; p < 0.001; n = 454 participants] (Figure 7). We had incomplete data on those who partially adhered because these participants did not attend for repeat assessment of baseline tests (243/697, 35%).

FIGURE 7.

Change in CST score in participants who adhered to exercise intervention (n = 454).

Recorded data on total weight lifted (kg) and total repetitions were used to calculate work done (weight × repetitions) over time. This was analysed by major muscle group: the quadriceps, hamstrings and abductors. Data on work carried out were based on changes between first assessment and session 6 for full adherers (399/454). Among adherers, there was good evidence of an increase in muscle function over time in the major muscle groups: quadriceps MD 5.5 (SD 15.4; paired t-test p < 0.001), hamstrings MD 5.6 (SD 13.9; paired t-test p < 0.001) and abductors MD 5.1 (SD 13.8; paired t-test p < 0.001) (Figure 8; see also Appendix 1, Table 31). Weights were progressed in 190 out of 454 (42%) participants who complied with the intervention (among these, 32 participants had two or more increases in weights).

FIGURE 8.

Change in muscle strength (mean work done) over time among participants who adhered to exercise intervention (n = 454).

For those who partially adhered to the intervention, data on work carried out were based on changes between assessment and session 3 (159/243). A decline in muscle strength was found over time for all major muscle groups: quadriceps MD –2.4 (SD 8.4; paired t-test p < 0.001), hamstrings –2.1 (SD 7.2; paired t-test p < 0.001) and abductors MD –2.1 (SD 7.2; paired t-test p < 0.001) (Figure 9). Weights were not progressed in weaker participants.

FIGURE 9.

Change in muscle strength (mean work done) over time among participants who partially adhered to intervention (n = 243).

Change in balance over time

Balance problems were prevalent among those who attended exercise. Over half of participants (354/687, 52%) scored level 1 or 2 on the 4TBS on first assessment and, thus, either failed the test completely, being unable to stand with feet together, or achieved only some of the basic balance challenge (Figure 10). Mean scores improved from pre to post intervention in those who adhered to exercise [pre-intervention mean balance level 2.63 (SD 1.0); post-intervention mean balance level 3.22 (SD 0.9); MD 0.57, 95% CI 0.50 to 0.65; p = 0.001; n = 454 participants].

FIGURE 10.

Change in 4TBS levels over time in 454 participants who adhered to exercise intervention.

Perceived improvement at discharge

At the final assessment, therapists asked each participant about their strength, balance and walking since taking part in the programme (‘has your strength got better?’/‘has your balance improved?’/‘can you walk further or for longer?’). These data were available for participants who adhered to the exercise intervention and attended their final assessment (454/697, 65%). Two-thirds of participants reported improvements in their strength (271/454, 60%) and balance (270/454, 59%) at discharge (Figure 11). At the final appointment, therapists were asked to report their own opinion on whether or not participants had progressed. Progression was reported for 315 out of 454 (69%) participants; therapists thought that 122 out of 454 (27%) had not progressed (n = 17 missing).

FIGURE 11.

Perceived self-reported changes in strength, balance and walking among participants who adhered to the exercise intervention (n = 454).

Frequency of exercise at time of discharge

At the final appointment, participants who completed the intervention were asked how regularly they were exercising: 314 out of 454 (69%) participants reported exercising three or more times per week and the remainder were exercising fewer than three times per week. At the final appointment, all participants were given an information leaflet describing opportunities for exercise classes in their local community and were encouraged to engage with these.

Multifactorial falls prevention intervention

Onward referrals to other services

All 762 participants who underwent MFFP assessment were assessed for all risk factors. A total of 432 participants were given information leaflets containing advice on how to self-manage symptoms of postural hypotension, foot care and recommended footwear, and/or tip sheets about risk management in the home environment. Among the 762 participants who returned a medication screen, 459 (60%) were referred either to their own general practitioner or to the PreFIT-nominated general practitioner in the GP for a more detailed medication review (level 3). A total of 317 out of 762 (42%) participants either failed the TUG (took ≥ 14 seconds) or had balance or gait problems or fears about falling (among whom 299 were referred for PreFIT exercise therapy and 18 were referred to other services). Fifty-eight (8%) onward referrals were made to a falls service doctor based on concerns raised in the falls history interview or the postural hypotension check. In total, 762 completed assessments resulted in 971 referrals to other health-care professionals, for example general practitioners, consultant geriatricians, PreFIT exercise therapists, podiatrists, occupational therapists or opticians, or social services (Table 21).

TABLE 21 - Advice given and onward referrals from risk factors identified in 762 MFFP assessments

N/A, not applicable.

Participants (n = 84) advised to attend optician (not a formal referral) for detailed vision assessment and/or new spectacles.

Participants (n = 299) referred to the PreFIT exercise intervention.

Participants (n = 143) were given advice leaflets and recommended to seek private podiatry services, with 11 NHS referrals made via the GP.

Risk factor identified	Advice leaflet given	Falls service doctor	Referral to another service (n)						Total referrals (n)
			General practitioner	Physiotherapist	Podiatrist	Occupational therapist	Opticiana	Other
Falls history/red flag	N/A	50	28	17	0	0	0	4	99
Balance/gait	N/A	0	0	299b	0	0	0	18	317
Postural hypotension	62	8	40	0	0	0	0	1	49
Culprit medications	N/A	0	459	0	0	0	0	0	459
Vision	N/A	0	0	0	0	0	84	7	7
Feet/footwear	143	0	0	0	11c	1	0	5	17
Social/home environment	143	0	0	0	0	18	0	5	23

Checks of treatment referrals to other services

Based on 762 completed treatment logs returned to the research team, 459 full general practitioner-led medication reviews were recommended. GP staff members (nurses or receptionists) were required to arrange separate appointments for level 3 medication reviews, if these were not completed on the day of MFFP assessment. Based on completed treatment logs, 79 out of 459 (17%) reviews led to medication modifications by general practitioners and 339 out of 459 (74%) resulted in no changes; 41 out of 459 (9%) treatment logs had missing data.

On completion of intervention delivery, visits were made by the trial team to all 21 GPs randomised to MFFP to ascertain whether or not recommended referrals and actions had been completed and recorded in primary care electronic systems. We found that the recording of recommended actions and outcome was generally poor. We found documented evidence of action for only 36% of all treatments recommended in the MFFP (e.g. referral to, or outcome of attendance at, other services such as falls specialists, physiotherapy, consultant-led eye services). For medication reviews, there was documented evidence in GP records that among the 459 general practitioner-led reviews only 238 had been completed (52%) (Table 22).

TABLE 22 - Verification of MFFP onward referrals from GP record searches in 21 MFFP GPs

OT, occupational therapist.

Referral	Falls history	Balance/gait	Medication review	Postural hypotension	Vision	Feet	Social/home	Total
Service referral	Falls doctor/general practitioner/other	PreFIT physiotherapist/other	General practitioner	Falls doctor/general practitioner/other	Optician/eye service	Podiatrist/OT/other	OT/social services/other
Referral confirmed in GP records, n (%)	18 (18)	56 (18)	238 (52)	10 (20)	4 (57)	15 (88)	10 (43)	351 (36)
No confirmation found in GP records, n (%)	81 (82)	261 (82)	221 (48)	39 (80)	3 (43)	2 (12)	13 (57)	620 (64)
Total referrals, n (%)	99	317	459	49	7	17	23	971 (100)

It was possible to compare findings of searches of GP records with accurate treatment data obtained from therapists for 203 participants who attended PreFIT exercise. There was evidence in GP records for only 56 out of the 203 (28%) onward referrals to PreFIT exercise. Therefore, GPs had no documented record of either referral or completion of treatment for > 70% of trial participants attending exercise therapy.

Referral to exercise therapy

Uptake of multifactorial falls prevention and exercise intervention

Among the 762 participants who had a falls assessment, 299 (39%) were referred for PreFIT exercise therapy. Uptake of exercise after the MFFP assessment was good, with 203 out of the 299 (68%) participants referred attending their first appointment. The median time from MFFP assessment to the start of exercise treatment was 6 weeks (IQR 3–11 weeks), ranging from 3 weeks (IQR 2–5 weeks) in Newcastle upon Tyne to > 16 weeks (IQR 5–22 weeks) in Birmingham and the Black Country. Time from randomisation to the start of exercise treatment was longer in the MFFP arm: on average 23 weeks (IQR 17–32 weeks) from randomisation. Participant characteristics of those who attended and declined are presented in Table 19.

Adherence

Among the 203 participants attending exercise therapy, 124 (61%) adhered to the full 6-month exercise programme (mean 6.2 months, SD 1.1 months). Those who partially adhered (79/203, 39%) received less than half of the recommended programme (median 12 weeks, IQR 4–17 weeks), similar to partial adherers in the exercise treatment arm (see Table 20).

Contacts with therapists

The 28 therapists had a total of 1022 contacts with 203 participants; over half of these contacts were face to face (584/1022, 57%) and the remainder were by telephone (438/1022, 42%). Although six contacts were recommended, 51 (25%) participants had seven or more contacts with their therapist. There were no differences in mean number of therapist contacts by region. Those who fully adhered to the programme had a mean of six (SD 1.0) contacts with their therapist and those who partially adhered had a mean of three (SD 1.5) contacts with their therapist. These findings are similar to findings for participants randomised to exercise without a preceding falls assessment.

Change in leg strength over time

Final test data were available only for those attending the final therapy session, that is, those who adhered to the intervention (124/203, 61%). The mean CST score was 3.2 (SD 1.2) pre intervention and 3.3 (SD 1.4) post intervention (MD 0.1, 95% CI –0.13 to 0.32; p = 0.39; n = 124 participants) (Figure 12).

FIGURE 12.

Change in CST over time among participants who adhered to MFFP and exercise intervention (n = 124).

Data on work done (weights × repetitions) were based on changes between assessment and session 6 for those who fully adhered to the intervention (105/124). There was evidence of a statistically significant improvement in muscle function over time for the major muscle groups, that is, for quadriceps (paired t-test p < 0.01), hamstrings (paired t-test p < 0.002) and abductors (paired t-test p < 0.001). Weights were progressed in 48 out of 124 (39%) participants who complied with the intervention (weights were increased once in 40 participants and twice in eight participants).

Data on work done over time were available for only 48 out of 79 (61%) participants who partially adhered to the intervention, from baseline assessment to session 3. A decline in muscle strength was observed over time, similar to that seen among those who partially adhered in the exercise intervention arm, although these findings are based on a small sample: quadriceps (paired t-test p < 0.09), hamstrings (paired t-test p = 0.11) and abductors (paired t-test p = 0.40).

Change in balance over time

Balance problems were common among these participants. Over half (114/198, 58%) scored only level 1 or level 2 on the 4TBS at first assessment, thus either failing the test completely or achieving only some of the balance challenge (Figure 13). An improvement in balance was observed over time, with a higher proportion of participants achieving levels 3 and 4 of the challenge post intervention (see Figure 13).

FIGURE 13.

Change in 4TBS over time among participants who adhered to MFFP and exercise intervention (n = 124).

Perceived improvement at discharge

At the final assessment, therapists asked each participant about their strength, balance and walking since taking part in the intervention. These data were available for those who adhered (124/203, 61%). The proportion of participants reporting improvements in strength (65%) was similar to the proportion who adhered, although a lower proportion reported improvements in balance (52%) and walking (44%) than those randomised to the exercise arm (Figure 14). Therapists were also asked to report their own opinion on whether or not participants had progressed at the final appointment. Progression was reported in 57 out of 124 (46%) participants; therapists thought that 25 (20%) participants had not progressed (n = 42 missing).

FIGURE 14.

Change in 4TBS over time among participants who adhered to the MFFP and exercise intervention (n = 124).

Frequency of exercise on discharge

Participants who completed the intervention were asked at their final appointment how frequently they were exercising: 77 out of 124 (62%) participants reported doing strength exercises three or more times per week and 77 (62%) participants also reported doing balance exercises three or more times per week (the remainder were exercising fewer than three times per week). As for those randomised to exercise, all participants were given an information leaflet, describing opportunities for exercise classes in their local community, at their final appointment and were encouraged to engage with these.

Process evaluation

Overview of health economics analysis

We did a within-trial economic evaluation, with the objective of estimating the cost-effectiveness of exercise and MFFP, both compared with advice. The economic evaluation took the form of a cost–utility analysis, expressed in terms on incremental cost per quality-adjusted life-year (QALY) gained, incremental net health benefit (INHB) and incremental net monetary benefit (INMB). The analysis is based on an NHS and Personal Social Services (PSS) perspective, using the reference cost framework as recommended by NICE. 116

Measurement of resource use and costs

The incremental costs associated with the trial were determined using a comprehensive strategy that encompassed two approaches: estimation of (1) costs associated with the delivery of the interventions and (2) secondary care costs and broader health and PSS resource inputs and costs.

Costing of the PreFIT active interventions

A specific focus of the economic evaluation was the assessment of the cost of delivering the interventions in the active intervention arms. Participants randomised to the control arm received only the Age UK Staying Steady leaflet. 22 Those allocated to exercise or MFFP were mailed a falls risk screener, which was returned to their GP. Participants at risk of falling were invited to attend for further assessment and treatment, either exercise or MFFP, depending on GP allocation.

The exercise intervention was adapted from the OEP. 72 The OEP intervention manual is free to download online, but slight modifications were made for delivery in the trial context (with permission from the Otago research group). The PreFIT intervention consisted of individual or group sessions with a trained therapist over 6 months. Two costing perspectives were adopted for costing the intervention: one that considers delivery of the interventions but which considers adaptation of the MFFP and exercise manuals for the purpose of the intervention to be sunk costs that not occur if treatments were rolled out nationally; and one that implies consideration of both the adaptation and delivery. Our base-case scenario is to use the delivery cost perspective and not include adaptation costs, although we consider both perspectives in a sensitivity analysis. Unit costs for intervention delivery staff include employers’ National Insurance plus a percentage of salary for employers’ contribution to superannuation. Costs included minimal time for data collection; production of the manual; cost of training the PreFIT team in the formal OEP training; health-care professional training; production of participant materials, including exercise booklets and exercise equipment; and staff time for the actual delivery of the intervention (Table 24). For the sensitivity analyses, costs of revising and adapting the exercise and MFFP manuals were included.

The MFFP assessment consisted of a single risk assessment session, after which participants were potentially referred to other health-care professionals and services based on their assessment results. The intervention was based on the Tinetti et al. 9 MFFP programme and modified for the PreFIT. The delivery of the MFFP intervention consisted of an individualised risk assessment conducted within a 1-hour appointment with a trained assessor.

The total cost of confirmed referral visits emanating from the MFFP assessment were included in the intervention costs (Table 25). It was possible to confirm attendances for those referred to exercise and for general practitioner-led medication reviews based on treatment logs. A decision was made to report the cost of adaptation, manualisation and delivering the interventions separately from the resource use cost. Possible double-counting of intervention data and self-reported resource use data was dealt with in the analyses. The mean cost per participant was calculated by dividing the total cost of the intervention by the total number of participants randomised to the treatment arm (Table 26).

Collection of secondary care use data

Data on inpatient hospital spells and A&E and outpatient attendances over the duration of the trial were sourced from HES data provided by NHS Digital for financial years 2011/12 to 2015/16. As these data are obtained centrally and not self-reported, we assumed that these data were complete. The completeness of hospital data and the ability to identify when participants had prolonged periods in hospital and/or A&E visits was exploited in the MI approach used to estimate missing self-reported responses. Inpatient spells during the study and other hospital-based care costs were determined by linking HES data with 2015/16 Healthcare Resource Groups (HRGs), using 2015/16 reference cost grouper software from NHS Digital, and then costed using NHS Reference Costs 2015–2016. 123 Inpatient and outpatient spell costs included unbundled costs (such as high-cost drugs) and excess bed-day costs, when applicable. All costs were discounted using a rate of 3.5% per annum.

Collection of broader resource use data

We collected data on health-care resource use from participant self-report questionnaires from randomisation to 4, 8, 12 and 18 months post randomisation. Data included use of primary and community-based health care, community-based social care, residential or nursing care and aids and equipment for each time point. Broader health-care resource use costs were valued by applying unit costs from the Personal Social Services Research Unit (PSSRU) national tariffs. 120 Mean annual equipment costs include the costs of installing equipment and making adaptions. The Hospital and Community Health Services index was used to adjust costs, when necessary, to 2015/16 prices. 121 All costs were discounted using a rate of 3.5% per annum. Resource use values at the individual participant level were combined with unit costs for each resource item to estimate economic costs for resource use (Table 27).

Calculation of utilities and quality-adjusted life-years

The economic evaluation estimated QALY profiles for trial participants, based on participant reports of preference-based HRQoL outcomes at each follow-up time point, using the EQ-5D-3L. 127 The EQ-5D-3L uses a descriptive system that defines HRQoL across five dimensions: (1) mobility, (2) self-care, (3) usual activities, (4) pain/discomfort and (5) anxiety/depression. Responses in each dimension are categorised into three ordinal levels: (1) no problems, (2) some or moderate problems and (3) severe or extreme problems. For the purposes of the economic evaluation, the UK time trade-off tariff was applied to each set of responses to generate an EQ-5D-3L utility score (preference weight) for each trial participant. 127 Participants who died during the study were allocated a utility of zero at all following time points. QALYs were calculated as the area under the baseline-adjusted utility curve and were calculated using linear interpolation between utlity scores at baseline and 4, 8, 12 and 18 months. QALYs were discounted using a rate of 3.5% per annum.

Missing data

Multiple imputation using the method of chained equations was used for the base-case analysis to impute missing data. This avoids potential biases associated with complete-case analysis and is consistent with good practice guidance. 128 Data were assumed to be missing at random. MI of missing self-reported HRQoL data and self-reported costs was conducted on a full analysis data set of combined baseline data, self-reported HRQoL and costs and complete HES data. The inclusion of the HES data and the ability to condition imputation on observed and assumed complete secondary care use make the assumption that data were missing at random more plausible. The MI analysis was conducted using the PROC MI command in SAS® software version 9.4 (SAS Institute Inc., Cary, NC, USA). A total of 100 imputations were calculated.

Results

Secondary care and broader resource use costs

Figures 14 and 15 show observed average complete-case secondary care costs and imputed broader resource use over time by treatment arm. Table 24 presents the corresponding average regression-corrected monthly costs, respectively, for secondary care and broader resource use costs by trial allocation and study period. Owing to the well-balanced nature of the trial, the regression analyses, which accommodate the clustering of outcomes within patients and practices over time, do not substantially change the picture that emerges from inspection of the raw and imputed data (Table 28). We see that secondary care monthly costs exceed resource use costs and both show an increasing trend over time (Figures 15 and 16). The trend appears more pronounced in the secondary care data. Inspection of the HRGs in the secondary care data demonstrates that the majority of secondary care costs are not directly related to falls, but rather to other comorbidities (e.g. cancer) (see Appendix 1, Table 33).

TABLE 28 - Secondary care and resource use monthly costs over time

Time point	Advice		Exercise		MFFP
	Mean (£)	% imputed	Mean (£)	% imputed	Mean (£)	% imputed
Secondary care
–4 months to randomisation	115	0	108	0	100	0
Randomisation to 4 months	126	0	112	0	116	0
4 to 8 months	152	0	137	0	147	0
8 to 12 months	172	0	162	0	161	0
12 to 18 months	172	0	167	0	165	0
Resource use
Randomisation to 4 months	49	8.2	43	8.3	47	8.9
4 to 8 months	50	12.3	50	13.4	53	13.2
8 to 12 months	52	15.9	47	16.5	55	17.6
12 to 18 months	60	22.0	62	23.6	61	23.8

FIGURE 15.

Secondary care monthly costs over time, complete cases.

FIGURE 16.

Imputed broader resource use monthly costs over 18 months.

The total expected average net present value (NPV) broader resource use costs over the 18-month period are £951.10 for advice, £924.52 for exercise and £971.23 for MFFP. For secondary care, the modelled expected average NPV secondary care costs are £2785.66 for advice, £2747.79 for exercise and £2930.25 for MFFP. Although MFFP is systematically related to higher expected costs than either advice or exercise, regression results showed no statistically significant differences between the trial groups at any time point in any cost regression (see Appendix 1).

Total costs

Total costs consist of intervention, secondary care and broader resource use costs. The expected average NPV total costs for each intervention are £3737.42 for advice, £3720.44 for exercise and £3940.92 for MFFP. Resource use and secondary care costs account for by far the greatest component of total costs, and much more than intervention costs, with approximately 75% of all costs being secondary care costs (inpatient, A&E and outpatient attendance). Including the sunk costs of manual adaptation raises the expected costs to £3721.59 for exercise and £3946.35 for MFFP (relatively modest increases over the 18 months).

Inspection of the modelled regression-based expectations for participants over time shows a similar gradual increase in costs over time for all interventions, with MFFP having a small but systematically higher incremental cost at all time points. There is almost no distinguishable difference in modelled expected costs over time between advice and exercise (Figure 17).

FIGURE 17.

Modelled regression-based monthly costs over 18 months.

Over 18 months, a participant in the MFFP arm is expected to generate an incremental cost of £204 relative to advice and of £220 relative to exercise. The cost per participant of the advice leaflet is only £17 more than the cost of exercise (equivalent to approximately £1 per month). These are very marginal differences and, from the regression results, and we know that they are measured with a relatively large SE.

The mean expected values over the PSA simulations are £3739.83 for advice, £3713.42 for exercise and £3942.76 for MFFP, leading to almost identical incremental differences in costs as calculated in the deterministic model.

For complete-case analysis, the total costs are £3506.98 for advice, £3492.38 for exercise and £3683.12 for MFFP. These figures are lower than those obtained from the imputed data because data for self-reported resource use are more likely to be missing when there are A&E attendances or longer hospital stays. Imputation on a data set containing HES data leads to higher than overall average costs being imputed when missing; this is intuitively appealing because the expectation is that all resource use costs will be higher when A&E admission occurs. The incremental differences are similar but slightly smaller, with MFFP being the most expensive intervention, followed by advice. For example, the incremental costs of MFFP relative to advice and to exercise are now £30 smaller, at £176 and £191, respectively. Between advice and exercise, advice is more costly by £15.

Health-related quality-of-life outcomes

Figure 18 shows the pattern of EQ-5D-3L utility values over 18 months. Table 25 shows mean EQ-5D-3L values by trial allocation and by study period. The three intervention arms show EQ-5D-3L mean utility values decreasing at each time point. The regression results for the imputed EQ-5D-3L utilities show no statistically significant differences between exercise and advice, and between advice and MFFP, but there are statistically significant differences between exercise and MFFP at months 4, 12 and 18, with exercise having small but systematic incremental gains in self-reported HRQoL (0.011 at months 4 and 12 and 0.0016 at month 18). The complete-case regressions yield very similar results (Table 29).

FIGURE 18.

Pattern of EQ-5D-3L utility over 18 months.

TABLE 29 - EuroQol-5 Dimensions, three-level version, utility by treatment arm over time

Time point	Advice		Exercise		MFFP
	Mean	% imputed	Mean	% imputed	Mean	% imputed
Randomisation	0.774	4.4	0.778	4.4	0.765	5.2
4 months	0.761	11.7	0.772	11.7	0.748	11.8
8 months	0.753	15.5	0.758	15.6	0.741	16.4
12 months	0.745	18.6	0.754	19.5	0.730	20.1
18 months	0.730	24.2	0.739	24.8	0.711	25.6

Modelling regression-based expectations over time, Figure 19 shows a consistent pattern, with exercise, followed by advice, yielding the highest HRQoL at all time points. Although it is difficult to extrapolate with within-trial models, the gap between exercise and MFFP appears to be increasing over time, leading to the expectation that if the trial period were to be extended we would see an increasing incremental difference. The NPV QALYs show an average expectation of 1.1137 for advice, 1.1195 for exercise and 1.1064 for MFFP, leading to a small incremental gain of 0.006 QALYs for exercise relative to advice and a further gain of 0.007 QALYs relative to MFFP. The incremental difference between exercise and MFFP over the 18 months of 0.013 is approximately equivalent to an additional 5 days in perfect health.

FIGURE 19.

Modelled EQ-5D-3L utility over 18 months (AUC).

Complete-case analysis estimates expected NPV QALYs of 1.1166 for advice, 1.1206 for exercise and 1.1082 for MFFP. Although the expected NPVs are higher in all cases (a function of missing EQ-5D-3L data being most likely when hospitalisations occur, with imputation correcting for this via imputation with HES linked data), the incremental differences are similar and the substantive picture does not change. PSA-simulated means for the imputed data are 1.1136 for advice, 1.1193 for exercise and 1.1063 for MFFP, in all cases almost identical to the figures in the deterministic model.

Cost-effectiveness results

Sensitivity analyses

The key finding from the analysis is that exercise dominates both advice and MFFP in terms of producing higher QALYs at a lower expected cost. However, we also recognise that the practical differences between the QoL and costs estimated between treatment choices are modest, particularly between advice and exercise. For example, we expect an incremental £1 per month cost difference between advice and exercise. Given that the patient population is extremely heterogeneous in terms of underlying QoL and pre-baseline costs, and had in excess of 20% missing data for HRQoL at month 18, it is important to assess the extent to which the economic conclusions are robust to the sampling variation and variation due to MI that can occur when applied to a heterogeneous population and addressing missing data. Despite these issues, we may still be relatively certain about the substantive conclusions, because the trial size was very large, the heterogeneity was well balanced across arms and there was consistency in findings across time.

Probabilistic sensitivity analysis combined with further critical appraisal of results provides the main opportunity for assessing the robustness of the results. In PSA, alternative estimates of parameter values for utility and costs under each treatment arm are generated and repopulate the existing model structure to produce alternative possibilities of expected QALYs and costs and, hence, incremental values. As with MI, there are a set number of alternatives drawn and the incremental outcomes recorded. Inference is then drawn by looking at the results across the population of alternatives, looking at the distribution of incremental outcomes via a cost-effectiveness plane and the proportion of times each intervention looks cost-effective at various threshold values via a CEAC.

The alternative parameter values for costs and HRQoL at each time point under different treatments were drawn from the regression estimate and variance–covariance matrices, which in combination define probabilistic distributions for estimated parameters. As the variance–covariance matrices accommodate uncertainty from both sampling and imputation processes, it was considered a more efficient method than bootstrapping across all imputed data sets and more accurate than just bootstrapping across a complete-case data set. The notion of conducting PSA from the regression variance–covariance matrix was pioneered by Hoch et al. 130 Simulation via the relevant regression matrices was conducted using PROC IML of SAS and 10,000 simulations were conducted.

Figure 20 shows the simulated costs and QALYs outcomes of MFFP and exercise relative to advice. The figure in the bottom right of the composite diagram shows the standard scatterplot of costs and QALYs from the same simulation, with advice at the (0, 0) reference point and threshold lines annotated over the plot. In addition to the scatterplot, we have also included univariate histograms of the simulated incremental costs and QALYs in isolation: the cost histograms are to the right of the scatterplot and the QALY histogram is located above the scatterplot.

FIGURE 20.

Cost-effectiveness plane of MFFP and exercise (vs. advice). Purple shading indicates overlap of MFPP and exercise interventions.

The cost-effectiveness scatterplot shows the two clouds of simulated paired incremental outcomes of MFFP and exercise relative to advice. The MFFP cloud is mainly located in the north-west quadrant, indicating that it is less effective (by being on the west side) and more expensive (by being on the north side) than advice. The clear majority of simulated points lie to the left of the threshold lines, indicating a high probability that MFFP is not cost-effective relative to advice. We note that some of the simulations lie in the south-west quadrant and to the right of the threshold lines, indicating that some simulations find MFFP cost-effective relative to advice, on the grounds that, although it is less effective, the expected cost-savings would be of a magnitude that allowed the freed-up costs to more than offset the QALY loss to the patients receiving MFFP. The cloud of simulations for exercise relative to advice is on the east side of the graph, indicating a relatively high degree of certainty regarding the incremental QALY gain, but the points are split fairly evenly over the north and south quadrants, indicating a large degree of uncertainty regarding the incremental cost estimate.

The univariate histograms reinforce this perspective. If we consider the cost histogram, we notice several elements. First, the distribution of incremental costs of exercise relative to advice is virtually centred over zero and has relatively wide tails stretching from being £894 more expensive to £903 cost saving; a relatively wide distribution. Second, we also notice that the distribution of incremental costs of MFFP relative to advice is fairly wide and, although the body of the distribution indicates that MFFP is more expensive, there is some substantial proportion of the distribution indicating a potential cost saving outcome.

The QALY perspective looks more certain. The range of simulated QALY differences is quite small and, importantly, there are clear differences between the central locations of the distribution and that of zero incremental effect. The PSA shows that, although the magnitude is small, the uncertainty stemming from sampling a heterogeneous population with imputation is relatively minor, with only a small possibility that MFFP is more effective than advice and that advice is more effective than exercise.

The CEAC provides a consolidated means of summarising these findings. At the left-hand side of the graph, where we value QALYs at £0, the economic argument is solely driven by the cost argument. The uncertainty is so substantial that it is almost impossible to determine whether advice or exercise is the most cost-effective. Indeed, the uncertainty is so substantial that there is a non-zero possibility that MFFP is the most cost-effective.

However, as we move along the x-axis and afford greater value to the QALY component of the cost-effectiveness argument, the greater certainty that have that the estimates of incremental QALYs will start to have a bigger impact. Overall, the CEACs show that exercise is always expected to be the most cost-effective intervention (Figure 21), ranging from a 50% probability based on costs alone to 80.5% as QALYs dominate. Over the conventional range of amounts that decision-makers are willing to pay for an additional QALY, that is, between £20,000 and £30,000, the probability that the exercise intervention is cost-effective varies between 70% and 75%, driven by the incremental differences in QALY expectations. Over that range, MFFP is the most cost-effective option approximately 1% of the time.

FIGURE 21.

Cost-effectiveness acceptability curve baseline analysis.

This allows us to draw a number of conclusions. First, there is some substantial uncertainty in the incremental cost differences between all treatments and at face value those uncertainties translate to reasonably substantial cost differences. Second, there is relatively more certainty within the QALY calculations. Third, and as a consequence of the first two conclusions, as the ‘value’ of a QALY increases, the more certain QALY component starts to exceed any possible differences in costs. As a result, we can conclude that the uncertainty in costs is not particularly important in establishing which is the most cost-effective treatment. The CEACs rise more steeply before the £20,000 threshold and more broadly flatten beyond this. Therefore, resolving the uncertainty we have in costs has merit only if we value QALYs < £20,000.

In addition to the imputed model, PSA was also conducted on the complete-case analysis as a sensitivity analysis. The probability that the exercise intervention is cost-effective is lower than in the baseline analysis, ranging from 64.5% to 68.5% between £20,000 and £30,000 thresholds, and asymptotes at 73% as the threshold of willingness to pay increases.

Discussion

The trial-based economic evaluation is based on a large randomised trial with well-balanced cohorts at baseline. Although there was an increasing pattern of missing self-reported data on broader resource use and HRQoL over time, the pattern was very well balanced across interventions. MI was used to address missing data and was further bolstered by the addition of what is considered complete secondary care data (using HES), which allows us to estimate broader costs and HRQoL on fully observed patterns of secondary care use. Multilevel linear regression models were used to accommodate the clustering of results of HRQoL, broader resource use and secondary care costs within participants within GPs and provided regression-corrected expectations for use in the economic modelling. Although the modelling shows substantial differences between individuals, in terms of expected costs and QALYs over time, the well-balanced nature of the cohorts and large sample size mean that simply comparing raw sample averages across interventions gives a generally valid impression of relative effectiveness. Because missing data are more likely when there are large secondary care costs and when the participant has an A&E episode, and, as observed, HRQoL is generally lower and broader resource use costs are generally higher when there is an A&E episode, imputation has the effect of lowering QALYs and increasing costs. However, as the pattern of missingness is very similar across treatment arms, the impact of imputation on the incremental differences between interventions is minimal.

We found increasing costs over time in all interventions for both broader resource use and secondary care costs. The increase is greatest in secondary care costs, which dominate the overall cost generation, with approximately 75% of costs occurring here. Examination of the HRGs associated with the secondary care costs shows that the majority of these costs are unrelated to falls, being mainly due to chemotherapy (see Appendix 1). Overall, there is an expectation that exercise generates lower costs than advice, which generates lower costs than MFFP. Although the differences are small (approximately £1 per month for exercise vs. advice, rising to £12 per month for exercise vs. MFFP) and not statistically significantly different, the pattern is consistent, with MFFP being the most costly intervention at all time points. There was virtually no difference in expected costs between advice and exercise.

There was a more observable difference in incremental QALYs between interventions, although the order is the same and as consistent, with exercise delivering the highest HRQoL over time, followed by advice and then MFFP. The incremental differences between interventions appear to be increasing over time and the regression model finds small but statistically significant differences between exercise and MFFP at months 4, 12 and 18. In all cases, HRQoL is falling over time. The incremental difference between exercise and MFFP is 0.013 QALYs (approximately an extra 5 days in perfect health spread over 18 months). For exercise compared with advice, this is 0.006 QALYs.

Applying these expectations in the NICE reference case framework finds that, as exercise produces the highest expected QALYs and the lowest expected costs, it dominates both advice and MFFP. Similarly, advice dominates MFFP. However, although the ordering and conclusions are clear, the magnitudes of the INHB and INMB are small, indicating marginal practical differences in cost and QALY outcomes between interventions. The INMB between exercise and MFFP valued at £30,000 per QALY is £613 and between exercise and advice the equivalent figure is £191. These results are robust to using complete-case analysis and the means from the PSA.

The PSA is driven by uncertainty in the regression results, as captured by the variance–covariance matrix. The impact on the imputed results of PSA is to suggest that exercise is the most cost-effective treatment approximately 67–75% of the time as the willingness to pay for a QALY rises from £20,000 to £30,000. At the extremes, when either costs (willingness to pay = £0) or QALYs (maximum willingness to pay is infinite) dominate the evaluation, the figure is 49–81%. MFFP is almost never simulated to be cost-effective as the willingness to pay increases. The probability that MFFP is cost-effective is approximately 8% when QALYs are not valued and falls to 1% when the threshold value is £20,000 per QALY. Therefore, any uncertainty about the most cost-effective treatment is between exercise and advice.

Nevertheless, because of the large size of the trial and the balance across cohorts, the economic conclusion is largely robust to PSA, despite the heterogeneity and the impact of exogenous factors. Any uncertainty about the most cost-effective treatment is between advice and exercise, because MFFP is only very possibly cost-effective when only costs are considered (and even then, it is unlikely to be cost-effective). As the value of QALYs grows, it becomes more likely that exercise is the most cost-effective treatment. It is possible that the impact of exercise on HRQoL is not just related to the likelihood of falls.

Study findings and key messages

This trial is the first large-scale, pragmatic, definitive trial to investigate the clinical effectiveness and cost-effectiveness of different falls prevention interventions embedded within UK primary care services. 131 The trial was designed to reflect a significant and contemporary dilemma in UK health policy, which is whether or not to introduce systematic screening and linked interventions from primary care for falls prevention. We aimed to provide evidence to inform UK health-care practitioners about the options for preventing falls and fractures in older people. Using this population screen-and-treat model in a sample of older people, we found no statistically significant difference in fractures between treatment arms.

We found evidence of interim benefits in rate of falls and QoL outcomes in those undertaking exercise, and these findings, along with differences in secondary care usage, led to marginal cost-effectiveness benefits in the exercise group relative to MFFP. Overall, exercise dominated advice and MFFP, suggesting that exercise might be the most cost-effective intervention for future investment.

Key findings from the PreFIT contrast with recent systematic reviews reporting that these interventions lead to a reduction in falls and fractures, and we therefore consider possible reasons for these differences. Issues relating to internal and external validity of the trial are discussed, with consideration given to the characteristics of our participant cohort. We then consider the wider clinical and public health policy implications of our findings on falls prevention services in the NHS.

Internal and external validity

We approached > 29,000 older people and recruited almost 10,000 participants, with an age range spanning 30 years. GP deprivation was representative of practices across England, with a good sociodemographic spread of GPs recruited from more deprived inner cities to affluent urban, rural and semi rural localities. GP recruitment was staggered to avoid overburdening local services and spanned several years, allowing for seasonal variation in falls and hospital admissions. We used several strategies to maximise the efficiency of the study design, including controlling cluster size through random sampling in primary care and the use of random subsampling for falls data collection. The very large sample size and robust approaches to capture fractures and falls allowed for comprehensive analyses of outcomes and secondary analyses of treatment effects by important clinical covariates.

Data collection

We triangulated multiple sources of evidence for incident fracture events over time by purchasing multiple waves of national statistics of hospital attendances and admissions and by completing comprehensive searches of primary care records (62 out of 63 GPs). Hospital discharge letters and radiological reports were obtained for fractures reported in GP records, whenever possible. Self-reported fracture events were neither necessary nor sufficient in themselves for fracture confirmation. NHS HES data are widely used for epidemiological and health services research. Data quality and accuracy is reportedly higher in the more recent data sets (from 2011 onwards), and in the more established data sets (such as the acute patient care and A&E data sets that we used in our analysis). We used ICD-10 three- and four-digit diagnostic coding and carefully screened all relevant injury and falls codes as per the prespecified analysis plan. Accuracy of ICD-10 coding for primary diagnoses in HES has been reported at 96% (IQR 89–96%). The adjudication panel were blind to treatment allocation and thus we are very confident of low risk of ascertainment or detection bias for our primary outcome.

For secondary outcomes, two methods, retrospective (with a short time frame) and prospective, were used to capture participant self-reported falls outcomes. Using a within-trial, random-sampling strategy, we restricted prospective diaries to a 4-month period over the first year of follow-up, rather than over 12 months. This revealed a higher mean rate of falling per month reported on prospective diary cards and also a small, but important, impact of diary card allocation on withdrawals from the trial over time. Extending prospective falls data collection to 12 months, as recommended by international falls prevention groups, would have had a significant impact on study attrition and would have required postal administration of 117,648 diaries. Our innovative approach reduced burden on participants and administrative burden through unnecessary follow-up of falls outcomes that did not require as much statistical power for definitive analysis. Latest research testing wearable technologies for monitoring balance and falls is being undertaken in hospitalised patients; however, these technologies are in the early stages of development and their use in large population studies is not yet feasible. 132

Uptake to trial

We found that uptake to the PreFIT (33%) was reasonable compared with other trials recruiting older adults via primary care. Recent preventative lifestyle trials using similar methods to identify and approach older people via primary care yielded lower uptakes (e.g. the UK Lifestyle Matters RCT mailed 18,331 people aged ≥ 65 years and achieved 2% uptake). 133,134 Using a similar design, the high-quality UK ProAct65+ cluster trial,74 comparing OEP and FaME with advice, recruited 6% of 20,500 people aged ≥ 65 years invited from primary care. Our invitation letters referred to a study investigating how to remain fit and active, and how to prevent falls and fractures, rather than specifically mentioning exercise per se. People aged ≥ 80 years, sometimes termed the oldest old, are also under-represented in clinical trials and are considered a hard-to-reach population, despite being the fastest-growing age group in the UK. 135 One-third of our sample were aged ≥ 80 years (n = 3247), of whom 300 were aged ≥ 90 years on recruitment. We piloted materials with older people, using larger font size and clear instructions along with provision of a freephone telephone number to encourage participants to ring for help with completion of questionnaires and falls diaries. We believe that these strategies contributed to high uptake and retention over time.

Participant characteristics

Perhaps unsurprisingly, people who did agree to participate in the trial were fairly active, with approximately 90% being cognitively intact and self-reporting high levels of activity at baseline. Very few participants had substantial problems with ADL or severe mobility restrictions and we acknowledge that there may be a risk of healthy respondent bias. Nevertheless, despite being predominantly active and mobile, one-third of participants had fallen in the year prior to recruitment, suggesting that our sample were representative of community-dwelling older people. These data have changed little over the last 30 years: early epidemiological studies9,10 reported that up to one-third of older people fall once or more per year. The risk algorithm, using responses in the baseline questionnaire for the whole cohort, found that 44% of participants were considered at risk of falling. One-fifth of participants had symptoms of frailty (20%), predominantly sensory deficit problems, although the Strawbridge questionnaire is weighted towards measurement of sensory deficits. Frailty was correlated with older age, falls history and other characteristics. As with other falls prevention trials,74 those who were older and frail were more likely to withdraw from the trial, although we found no differences in either rate of mortality or withdrawals by treatment arm. Our treatment groups were well balanced across arms, although a non-statistically significant higher falls rate in those randomised to the multifactorial intervention was observed; this was due to five extreme fallers. When extreme observations were removed, fall rates in the three treatment arms were very similar.

Our population was predominantly white (98%) and, thus, under-representative of the overall black and ethnic minority population in England (UK 2011 Census:136 86% white). However, when age is taken into account, only 4.6% of the English population aged ≥ 70 years is non-white. The mean age at leaving school was 16.8 years. We did not record highest qualification after leaving school, although the UK Office for National Statistics data5 show that over half of adults aged ≥ 65 years left school without any formal qualification.

Quality of life

We followed validated scoring guidelines for all measures. QoL in the PreFIT sample was high compared with both US and UK population norms. Population normative scores on the SF-12 scale for adults aged ≥ 75 years are lower for physical health, but mental health scores are comparable to those in our trial cohort [population mean SF-12 PCS, 38.7 (SD 11.0); population mean MCS, 50.1 (SD 10.9)]. 32,137 In the ProACT65+ trial, mean SF-12 PCS and MCS scores were 36.9 (SD 6.6) and 48.8 (SD 6.3), respectively. 74 The PreFIT trial participants, therefore, had better physical health than and comparable mental health to other research populations of similar age, despite being an older cohort than recruited to ProACT65+. 74 Missingness was low at baseline for QoL scales but increased over time. Comparison of complete-case and imputed data (n = 460) for EQ-5D-3L scores used in cost-effectiveness analysis did not change estimates.

Screening in primary care

We found that risk screening in primary care was feasible and cheap to undertake; almost 90% of people approached by their GP responded to the fall risk screener. Utility of the screener was good and prediction was comparable to accuracy values reported in studies using longer, more complex, falls risk screening tools. A short, annual screening questionnaire administered to older people in primary care will yield good-quality information about falls and balance problems. The more challenging and controversial issue is how best to intervene once those at risk have been identified.

Referrals and uptake to intervention

We extended participant follow-up to 18 months to allow time for postal screening by primary care teams and risk stratification before arranging referrals to active treatment. Mean time to first treatment was approximately 8 weeks, although some localities took longer. However, overall, this was acceptable and likely to be reflective of current NHS services. The longer follow-up period of 18 months allowed for the capture of treatment effects on fracture and falls outcomes. Uptake of and adherence to trial interventions was very good and comparable with that seen in other falls prevention clinical trials. 138

Comparison with other studies

Interpretation of study findings

There are important issues to consider when interpreting our findings. Possible issues include failure of our screening process to identify those most at risk, targeting the wrong risk factors in the multifactorial intervention, delivering diluted interventions and concerns over intervention fidelity. We failed to detect a meaningful treatment effect, yet we may have identified the true treatment effect of these interventions on fracture outcomes. It is plausible that falls prevention services do not prevent fracture outcomes. Falls prevention interventions may reasonably prevent falls, but the causal pathway from falls prevention to prevention of fractures is perhaps weak. A range of contributory factors were identified during fracture adjudication, including comorbidity and alcohol, which were not targeted by our interventions.

Our multifactorial intervention assessed for the main risk factors tested in many other trials and for which there was some suggestion of an association with increased risk of falling. Over 400 risk factors have been identified for falling. Multifactorial assessments are complex interventions and involve many dimensions of complexity, as recognised by the Medical Research Council. These include multiple interacting components, multiple behaviours required by those delivering and receiving the intervention, more than one organisational group targeted by the intervention and extent or degree of flexibility permitted (e.g. potential variability when interviewing older people, staff skill mix in different NHS settings). Our underlying assumption was that assessors, general practitioners and older participants would adhere to all prescribed behaviours and act on recommendations for onward referral and treatment. We closely monitored all referrals to exercise and to other health-care professionals and general practitioner-led medication reviews, but it was not possible to track whether or not participants acted on all other advice and recommendations for behaviour change. Despite attempts to control and standardise interventions, we recognise the influence of underlying contextual factors on the delivery of a complex intervention, such as multifactorial falls assessment. Another notable finding was that less than half of participants (42%) attending MFFP were referred for exercise therapy, despite these participants being considered at risk of falling based on their self-completed risk screener. Clinical teams were asked to assess gait balance problems using two methods: (1) the TUG and/or (2) visual assessment for any balance and gait problems or fear of falling. This is entirely consistent with the original Tinetti et al. 9 model of MFFP (observe gait and balance during transitioning). 141

Importantly, we found that the point estimate of the effect was for an increased fracture risk. For the MFFP comparison, this approached statistical significance. Therefore, a radically different approach is needed to effect a meaningful change in fracture rates. Simply using ‘stronger’ interventions or improving adherence to the interventions is likely to increase the existing trend to increased fractures in the intervention groups.

Intervention fidelity

Intervention fidelity is an important consideration in multicentre intervention trials. Complex interventions have more scope for variation in delivery and are more vulnerable than simple interventions to failure to implement components as they should be implemented. 142 However, we developed clear protocols and treatment pathways for each risk factor and had substantial expert input into developing interventions. Several elements of the falls assessments were novel for primary care staff (e.g. falls interview and the Snellen eye chart test). Every staff member received training from a geriatrician, and assessors were required to successfully undertake assessments of every risk factor before training certificates were signed. We provided clear supportive materials (e.g. laminated prompt sheets of interview questions, listings of culprit drugs to screen for during medication reviews). Quality of training delivered was assessed during the pilot phase and adaptations were made to include simple and complex case study examples. We did not undertake video-recording of face-to-face participant consultations, nor did we tape or video-record general practitioner discussions with trial participants about medication changes, which may have provided more insight into quality of delivery. Intervention staff members were observed by a trained assessor, as per usual recommended evaluations checks of fidelity for complex intervention trials.

We are confident that the MFFP intervention was delivered as recommended, albeit within the limitations of existing NHS services. Access to NHS podiatry was very limited and so participants were advised to book appointments with a private chiropodist or podiatrist in regions with excessively long waiting lists; the exception was for people with diabetes who could be directly referred to NHS diabetic podiatry services. It was not possible to trace non-health-care referrals or attendance at private podiatry or opticians for eye checks (free to those aged ≥ 70 years). One region delivered consultant-led geriatrician services and in other regions assessments were undertaken in primary care by nursing staff or by non-consultant falls teams. This non-geriatrician model is currently recommended by the British Geriatrics Society, which suggests that any member of the primary health-care team should be able to co-ordinate a comprehensive geriatric assessment; thus, nurses, general practitioners and pharmacy staff members can all undertake medication reviews. 143,144 Only complex cases should be referred to a geriatrician. 144 We undertook post hoc exploratory analysis on skill mix for multifactorial delivery, finding no effect. Therefore, the PreFIT MFFP intervention followed a service model not only recommended in 2011 at trial launch but also recommended and used in many clinical services today.

Medication reviews

Medication reviews are complex interventions in themselves and can be challenging and time-consuming to carry out. The 2017 cluster Opti-Med RCT,145 testing clinical medication reviews in older people in primary care with ‘geriatric problems’ (including mobility problems, falls and fear of falling), aimed to reduce inappropriate drug use and found no difference in QoL, geriatric problems, satisfaction with medication or self-reported medication adherence. These intensive, detailed reviews were undertaken by expert teams, and specific recommendations were made to general practitioners. However, the authors found that only 41% of medication changes (442/1084) recommended by the expert team were implemented or partially implemented by general practitioners, who were significantly more likely to implement the addition of a drug than the cessation of a drug (47% vs. 35%, respectively; p = 0.002). 146 Intervention studies aiming to reduce inappropriate prescribing have not resulted in measurable changes in patient outcomes and it has been argued that efforts should focus on high-risk patients. Willeboordse et al. 146 report a low uptake of recommended medication changes comparable to rates found in other studies (less than half of recommendations are actioned), although this can be higher if the patients’ own general practitioner is involved in the screening process. 146 Other research on polypharmacy and multimorbidity has found that general practitioners’ medication management strategies vary, leading to differences in proposed medication changes. 147

Fracture prevention

Although we have evidence of interim benefits from secondary outcomes and process evaluation to suggest that HRQoL and leg strength improved, this did not translate into a reduction in number of fractures. It is plausible that risk of harm is higher from exercise than multifactorial interventions owing to increased mobility and encouragement of activity. The ProAct65+ trial148 examined bone density changes in those who undertook two falls prevention exercise programmes (OEP and FaME) but found no effect on bone mineral density or bone structural parameters. The ProAct65+ trial148 suggested that 6 months of exercise intervention was insufficient to lead to bone mineralisation changes and that a greater magnitude of progressive loading and/or longer duration of exercise was required to achieve changes in bone density. 148 Recent expert statements for patients with osteoporosis recommend impact exercise, in addition to strength exercise, to promote bone health. 149

Strengths of the study

Undoubtedly, the major strength of PreFIT is the size and rigorous quality of the trial: it is the largest population-based fracture and falls prevention study carried out in the UK setting. The cluster trial design, in which clusters are assembled and participants randomly sampled and enrolled prior to randomisation, provides methodological rigour and avoids contamination bias. We obtained individual signed informed consent rather than gatekeeper consent, as per good practice guidelines for cluster trials. 19 We carefully tracked uptake, screening and referral to treatments at different stages. Treatment protocols were developed with experienced geriatricians and carefully piloted and tested before roll-out. We triangulated multiple data sources to ascertain the primary outcome. We achieved high follow-up rates over 12 and 18 months for all secondary outcomes and used strategies to capture falls outcomes prospectively and retrospectively. Importantly, imputation for missingness on QoL outcomes used for cost-effectiveness analyses did not change utility estimates.

Limitations of the study

We observed a lower than anticipated fracture event rate for the population, but this was offset by exceeding our planned sample size of 9000 by 9%, increasing follow-up time and achieving 99.9% data collection for the primary outcome. This allowed all prespecified statistical analyses to be completed with certainty. Our total fracture event rate, even within individual intervention arms, exceeds aggregated values reported in recent systematic reviews. The rate of observed fractures was lower than in the original sample size estimate. However, we used more efficient statistical techniques than originally planned to account for the lower fracture rate. We used linear mixed models, rather than generalised estimating equations, to account for data format and overdispersion. As with cluster randomised designs,150 there is a small risk of baseline imbalance in GP characteristics, because we did not incorporate stratification. This would have been challenging to achieve, given the requirements for inclusion (i.e. ability to provide intervention or access to services in the locality that could provide interventions). We adjusted for GP deprivation and examined GP characteristics by treatment arm.

Patient and public involvement

We included a lay member on one external committee, who provided input at all stages. Trial materials were developed and piloted in the early stages, with older volunteers attending a lunch social club in Coventry. In November 2018, we hosted a patient dissemination event at the University of Warwick to feed back results to a sample of participants. Owing to trial size, it was not possible to invite all participants and therefore invitations were sent to those resident in Warwickshire, with confirmation given to the earliest respondents. A total of 48 older people and their partners or carers attended the event. This event proved very successful and participants were very keen to learn of study findings.

Cost-effectiveness findings

We found small but relatively systematic and consistent differences between the expectations in QALYs and costs for the three interventions. Evidence across all time points suggests that exercise produces higher expected QoL and lower costs than either advice or MFFP. We noted that the population of participants was highly heterogeneous, with marked differences in expected costs and QALYs over time between individuals. Inspection of the secondary care data indicated that the majority of the secondary care costs were unrelated to falls and attributable instead to cancer treatment and cataract surgery procedures. Although it is not possible to demonstrate, one can surmise that QoL is equally highly influenced by the presence and/or treatment of cancer.

This heterogeneity was mitigated by the trial being appropriately powered and extremely well balanced across arms, such that the impact of heterogeneity is minimised. In addition, random-effects models were used to inform the economic models and accommodate patient-level heterogeneity in the health economic analysis.

The results from the cost-effectiveness analysis are consistent with a recent literature review conducted to identify cost-effective interventions to prevent falls in older people living in the community and published by Public Health England. 82 The review found that the majority of studies assessing the cost-effectiveness of multifactorial assessments reported negative results. Evidence on exercise was, however, mixed and related mainly to group-based interventions studies.

The main strength of the health economic analysis was that the trial was prospectively designed for a cost-effectiveness analysis using individual-level data on a very large number of older individuals well balanced across intervention arms. Costs and outcomes were carefully considered in the trial design, with the purpose of reaching a robust conclusion with respect to cost-effectiveness. The main limitation was that the analysis was limited to the trial horizon and the potentially large impact of exogenous factors on costs and QoL, which means that, even with a sample size this large, there is some non-marginal uncertainty left. Several factors were fundamental to our decision not to construct a decision-analytic model. The observed time period was sufficient to draw inference on the relative cost-effectiveness of each treatment and there was, therefore, no underlying need to extrapolate the results over time (i.e. the conclusions would not be changed by taking a longer-term perspective).

The underlying structure of costs and QALY generation, which we initially assumed to be mainly driven by falls and fractures, was less well established than expected and became less certain over the duration of the project. We do not believe that the differences in QALYs we observed were solely generated by an impact on falls and fractures and therefore do not believe that a model structured around falls and fractures captures the impact. As the relative pattern of QoL over time is consistent and increasing in incremental differences, we do not believe that extending the duration of observation over a longer time frame would change the results.

Future recommendations

Falls and fracture prevention remains an important target of preventative health care. Exercise remains the most promising intervention for primary care. However, future work should focus on improving uptake and adherence to strength and balance programmes within a broader framework or focus on a family of interventions to target geriatric syndromes.

Conclusions

In conclusion, the PreFIT tested the delivery of alternative fall prevention strategies embedded within the UK primary care setting and found that neither a multifactorial falls assessment intervention nor exercise reduced fractures in older people living in the community. We found no differential differences in fracture rates by sex, age or falls history in our subgroup analyses. We found an interim reduction in falls and small improvements in HRQoL in those randomised to exercise, compared with advice, but this was not sustained in the longer term. Nevertheless, the QoL benefits observed in the exercise arm dominated in the health economic analyses.

The PreFIT exercise intervention may reduce falls in the short term, but there is no evidence to support a reduction in falls over the longer term and no evidence for any reduction in fractures outcomes. The PreFIT MFFP intervention does not reduce rates of fractures or falls. The health economic results suggest that exercise therapy, when compared with advice and MFFP, is cost saving and has a small effect on overall QoL.

Trial Management Group

• Professor Chris Bojke (trial health economist).

• Professor Julie Bruce (research lead).

• Dr Susanne Finnegan (research physiotherapist).

• Mrs Susie Hennings (senior project manager).

• Dr Anower Hossain (trial statistician).

• Professor Claire Hulme (trial health economist).

• Dr Chen Ji (trial statistician).

• Professor Ranjit Lall (trial statistician).

• Professor Sarah E Lamb (chairperson).

• Dr Roberta Longo (trial health economist).

• Dr Laetitia Schmitt (trial health economist).

• Mr Craig Turner (trial administrator).

• Professor Martin Underwood (Professor of Primary Care Research).

• Mrs Emma Withers (trial manager/senior project manager).

Trial Steering Committee

• Professor Ann Ashburn (independent).

• Professor Julie Bruce (research lead).

• Mr Alex Bush (independent).

• Professor Ranjit Lall (trial statistician).

• Professor Sarah E Lamb (chief investigator).

• Professor Chris Salisbury (independent chairperson).

• Emma Withers (trial manager).

Data Monitoring and Ethics Committee

• Professor Opinder Sahota (independent).

• Professor Maria Stokes (independent).

• Professor Stephen Walter (independent chairperson).

Statisticians

• Dr Anower Hossain (trial statistician).

• Dr Chen Ji (trial statistician).

• Professor Ranjit Lall (lead trial statistician).

The trial team

General practices

Exercise intervention therapists

Multifactorial falls prevention intervention personnel

Provision of Age UK Leaflets

Age UK.

PreFIT Study Group

Contributions of authors

Julie Bruce (https://orcid.org/0000-0002-8462-7999) (Research Lead) was responsible for the study design, developed the protocol, developed interventions, was a member of the Trial Management Group and the TSC and was responsible for the writing and reviewing of the report.

Anower Hossain (https://orcid.org/0000-0002-1708-9940) (Research Fellow and Trial Statistician) was a member of the Trial Management Group, was responsible for the statistical analysis of the trial and was responsible for the writing and reviewing of the report.

Ranjit Lall (https://orcid.org/0000-0003-1737-2866) (Professor of Clinical Trials and Lead Statistician) was a member of the Trial Management Group, was responsible for the study design, was responsible for the statistical analysis of the trial and was responsible for the writing and reviewing of the report.

Emma J Withers (https://orcid.org/0000-0003-4832-9609) (Trial Manager and Senior Project Manager) was a member of the Trial Management Group, was responsible for the study design, was responsible for the running of the trial and was responsible for the writing and reviewing of the report.

Susanne Finnegan (https://orcid.org/0000-0001-8152-7610) (Research Physiotherapist) was a member of the Trial Management Group, was responsible for training of exercise intervention and was responsible for the writing and reviewing of the report.

Martin Underwood (https://orcid.org/0000-0002-0309-1708) (Professor of Primary Care) was a member of the Trial Management Group, was responsible for the study design and was responsible for the writing and reviewing of the report.

Chen Ji (https://orcid.org/0000-0003-4919-3299) (Research Fellow) was a member of the Trial Management Group, was responsible for the statistical analysis of the HES data and was responsible for the writing and reviewing of the report.

Chris Bojke (https://orcid.org/0000-0003-2601-0314) (Professor of Health Economics) was a member of the Trial Management Group, was responsible for the health economics analysis and was responsible for the writing and reviewing of the report.

Roberta Longo (https://orcid.org/0000-0002-9379-1627) (Research Fellow) was a member of the Trial Management Group, was responsible for the health economics analysis and was responsible for the writing and reviewing of the report.

Claire Hulme (https://orcid.org/0000-0003-2077-0419) (Professor of Health Economics), a former member of the Trial Management Group, was responsible for the study design and for the writing and reviewing of the report.

Susie Hennings (https://orcid.org/0000-0002-8160-6658) (Senior Project Manager), a former member of the Trial Management Group, was responsible for the writing and reviewing of the report.

Ray Sheridan (https://orcid.org/0000-0001-9169-6549) (Consultant Geriatrician) was responsible for the study design and for the writing and reviewing of the report.

Katharine Westacott (https://orcid.org/0000-0002-4039-9373) (Geriatrician) was responsible for MFFP training and for the writing and reviewing of the report.

Shvaita Ralhan (https://orcid.org/0000-0003-4699-228X) (Consultant Geriatrician) was responsible for MFFP training and for the writing and reviewing of the report.

Finbarr Martin (https://orcid.org/0000-0002-6911-3498) (Consultant Physician and Geriatrician) was responsible for study design and for the writing and reviewing of the report.

John Davison (https://orcid.org/0000-0002-0697-0820) (Consultant Physician) was responsible for the writing and reviewing of the report.

Fiona Shaw (https://orcid.org/0000-0002-9744-2977) (Consultant Geriatrician) was responsible for the writing and reviewing of the report.

Dawn A Skelton (https://orcid.org/0000-0001-6223-9840) (Professor in Ageing and Health) was responsible for the writing and reviewing of the report.

Jonathan Treml (https://orcid.org/0000-0002-6024-3544) (Consultant Geriatrician) was responsible for MFFP training and for the writing and reviewing of the report.

Keith Willett (https://orcid.org/0000-0001-8615-725X) (Professor of Orthopaedic Trauma Surgery and Director for Acute Care to NHS England) was responsible for the writing and reviewing of the report.

Sarah E Lamb (https://orcid.org/0000-0003-4349-7195) (Professor of Trauma Rehabilitation and chief investigator) was responsible for study conception and design and the writing and reviewing of the report and was guarantor.

Publications

Bruce J, Lall R, Withers EJ, Finnegan S, Underwood M, Hulme C, et al. A cluster randomised controlled trial of advice, exercise or multifactorial assessment to prevent falls and fractures in community-dwelling older adults: protocol for the Prevention of Falls Injury Trial (PreFIT). BMJ Open 2016;6:e009362.

Bruce J, Ralhan S, Sheridan R, Westacott K, Withers E, Finnegan S, et al. The design and development of a complex multifactorial falls assessment intervention for falls prevention: the Prevention of Falls Injury Trial (PreFIT). BMC Geriatrics 2017;17:116.

Finnegan S, Bruce J, Skelton DA, Withers EJ, Lamb SE on behalf of the PreFIT Study Group. Development and delivery of an exercise programme for falls prevention: the Prevention of Fall Injury Trial (PreFIT). Physiotherapy 2018;104:72–9.

Griffin J, Lall R, Bruce J, Withers E, Finnegan S, Lamb SE; PreFIT Study Group. Comparison of alternative falls data collection methods in the Prevention of Falls Injury Trial (PreFIT). J Clin Epidemiol 2019;106:32–40.

Lamb SE, Bruce J, Hossain A, Ji C, Longo R, Lall R, et al. Screening and intervention to prevent falls and fractures in older people. N Engl J Med 2020;383:1848–59.

Data-sharing statement

All requests for data should be sent to the corresponding author. Access to the available anonymised data may be granted following review.

Patient data

This work uses data provided by patients and collected by the NHS as part of their care and support. Using patient data is vital to improve health and care for everyone. There is huge potential to make better use of information from people’s patient records, to understand more about disease, develop new treatments, monitor safety, and plan NHS services. Patient data should be kept safe and secure, to protect everyone’s privacy, and it’s important that there are safeguards to make sure that it is stored and used responsibly. Everyone should be able to find out about how patient data are used. #datasaveslives. You can find out more about the background to this citation here: https://understandingpatientdata.org.uk/data-citation.

Disclaimers

This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health and Social Care. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health and Social Care.