Recipient organisationNIHR Applied Research Collaboration Yorkshire and Humber
NIHR supportRecorded as supported by this research centre
PeriodMar 2025 — Mar 2026
In plain English
AI plain-English summary
Falls among older adults cost the NHS billions each year, and a new computer model will forecast exactly how changes in the population—ageing, frailty, deprivation—will drive that burden up or down. This matters because current falls prevention relies on simple physical tests like the Timed Up & Go, which miss many at-risk people. The eFalls tool uses a wider set of patient characteristics to predict falls more accurately, but no one has yet modelled what that improved prediction would mean for NHS costs, patient outcomes, or the cost-effectiveness of different prevention programmes. The research will build two possible models. One simulates the entire population of England, running scenarios such as "what if the population becomes older and more frail?" to show how falls incidence and NHS costs shift. The other compares eFalls directly against existing screening methods in a primary care pathway, calculating whether using eFalls to identify high-risk patients and then directing them to proven interventions—Otago, FaME, Tai Chi, or home safety assessments—would save money or improve health. If successful, the work will give NHS commissioners a practical tool to decide where to invest in falls prevention, potentially reducing hospital admissions and keeping older people mobile and independent for longer.
View original technical description
As part of the ARC extension, the health economic theme will be undertaking decision-analytic modelling with the NIHR ARC-YH developed eFalls tool as the focal point. Currently, the modelling will focus on one of two options: Option 1) Patient-level simulation for forecasting falls impact on the NHS and patients in England. Forecasting will be based on an existing model by Kwon et al (2023). The eFalls predictors will be incorporated into the model, particularly as baseline population distributions. Based on these baseline distributions, the population of England will be simulated within the model with falls incidence based on the eFalls predictions. Scenario analyses will be run to represent the impact of falls on the NHS and patients based on changes in these baseline distributions, e.g., what if the population of England proportional became older/younger, more/less frail, more/less deprived. The scenario analyses will be based on expert opinion and existing forecasts/projections where possible (e.g., ONS projections). The simulation will be used to explain the economic and patient burden of falls based on changing population characteristics prognostic of falls. Option 2) A cohort-based Markov model economic evaluation of eFAlls in a falls-screening and falls prevention care pathway. The economic evaluation will be based on an existing model by Franklin & Hunter (2019). In essence, eFalls will be added as a third-arm in the model, to be compared against the Timed Up & Go (TUG) and Quantitative TUG (QTUG) as methods to assess falls-risk within primary care. The subsequent falls-prevention care-pathways are based on Otago, FaME, Tai Chi, or home safety assessment and modification. The model can be used to suggest the potential cost-effectiveness of eFalls as used within primary care compared to the commonly used TUG and more advanced (but not that routinely used) QTUG.
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