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Elderly people (65+) at risk of having falls and the people responsible for initiatives aimed at reducing falls, such as hospitals, wards or GP surgeries.


Using existing information on falls and fractures collected by health and social care services and analysing it in new ways to see if different ways of preventing falls actually work.


  • To develop a way to analyse and evaluate the effectiveness of new and historic service improvements (interventions) which aim to reduce falls and fragility fractures.
  • To see if data routinely collected in the community, by GPs for example, can be used to evaluate community (non-hospital) based fall prevention interventions.
  • To evaluate up to three new interventions as they are introduced in partner organisations of the Oxford AHSN Best Practice Falls Network.

Why this is important

The risk of having an accidental fall, and any associated complications such as broken bones, rises as people age.

Age-related changes in the body can lead to weaker bones than those of younger people, meaning that broken bones can happen easily. We call these 'fragility fractures'. UK hospitals see over 300,000 patients with fragility fractures per year.

The Department of Health estimates that about 35% of over-65s and 45% of over-80s experience one or more falls each year.  A further 10-25% of those are likely to sustain a serious injury. Such injury's often mark the point at which people can no longer live independent of help.

Health and social care services have all set targets to reduce falls, prevent fractures and improve the health and well-being of older people. As such, new services and innovations are regularly introduced to help prevent falls and fractures.

Yet, these innovations are often poorly evaluated. This means we can't be sure how effective they are in preventing falls and fractures, or saving money.

All hospitals in the UK are required to collect data on falls that occur there and have done so for some time. We may be able to use this existing real-life data as a 'natural experiment'.

By developing and applying statistical techniques to this routinely collected data – which the NHS lacks the resources to do – we may be able to tell how effective different interventions have been at reducing falls and fractures.

For community-based interventions, such as through GP surgeries, we may be able to use Hospital Episode Statistics (HES). This is data collected as people are admitted to hospital and includes the reason they were admitted, such as a fall. Using this we may also be able to determine how effective community-based interventions have been.


This research will explore the use of fall prevention 'natural experiments'. This will be done using existing pre and post-intervention data from local health organisations.

To do this an Interrupted Time Series (ITS) will be used. An ITS uses statistics to estimate and compare trends over time before and after an event. In this case, trends in falls and fractures before and after an intervention. This is better than comparing absolute numbers of falls which are likely to naturally vary over time, hiding the effects of an intervention.

Each organisation, or part of an organisation (e.g. a hospital or ward), will be analysed alone. The data used will span around two years either side of an intervention, with each month used as a single time point.

To examine if 'natural experiments' provide good enough data to properly evaluate interventions, HES data collected during, but not as part of, a clinical trial in community fall prevention will be used. Comparison of the two types of data (HES and clinical trial) should reveal any differences between the two.

This is especially important where clinical trials (the 'gold standard' for high-quality data) are not possible, worthwhile or workable.

How this could benefit patients

Identifying which interventions were most effective can guide the wider roll-out and use of the most effective methods. It can also be used to guide the development of new, even more effective, preventative measures.

This has the potential to vastly improve the quality of life of an ageing population.  Increasing the length of time that people are able to live independently in the community.

Additionally, this could reduce hospital admissions, hospital stay time, and avoid increasing pressure on care homes. All of which can free up resources and time for treatment and care of other conditions.

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