YHEC has been publishing impactful papers in the health economics field for decades. In this blog, we revisit one of our older publications, explore the research behind the paper, and consider why it has proved so influential.
Back in 2003, Dr Paul Scuffham was a Project Director at YHEC when he co-authored a paper with YHEC colleagues, Stephen Chaplin, and Rosa Legood. Titled “Incidence and costs of unintentional falls in older people in the United Kingdom”, the paper was published in the Journal of Epidemiology and Community Health.
What was the research about?
The research analysed the number of emergency department attendances, admissions to hospital, and costs associated with unintentional falls in people aged 60 or older in the UK. The study also estimated the costs of different types of falls, as well as the costs of falls by age group.
At the time, the costs associated with falls in the UK had not been widely researched, even though falls were recognised as an important independent predictor of admission to long-term care. There was also an increasing concern in public health about the impact of falls due to the ageing population.
How was the research done?
A standard economic framework was used to estimate the costs of serious unintentional falls to the NHS and Personal Social Services (PSS), defining serious falls as those resulting in injury requiring emergency department attendance or hospital inpatient treatment. National data from the Home Accident Surveillance System/Leisure Accident Surveillance System (HASS/LASS) and Hospital Episode Statistics (HES) for 1999 were used to estimate the incidence, outcomes and healthcare resource use associated with falls across four age groups (60 to 64, 65 to 69, 70 to 74 and ≥75 years).
HASS/LASS data were also used to estimate patient outcomes and additional resource use following emergency department attendance or hospitalisation, while HES data were used to estimate hospital admissions and inpatient costs. Costs included hospital treatment, emergency department attendances, follow-up GP and outpatient consultations, and long-term nursing home care, with assumptions made where reliable data were unavailable. A one-way sensitivity analysis explored uncertainty around the duration of long-term care attributable to falls and the number of follow-up GP and outpatient consultations.
What did the research find?
In 1999, there were 647,721 emergency department attendances and 204,424 hospital admissions for fall-related injuries in people aged 60 years and over in the UK.
The cost of unintentional falls per 10,000 population was £300,000 in the 60 to 64 age group, increasing to £1.5 million in the ≥75 age group. These falls were estimated to cost the UK government £981 million, of which the NHS incurred 59.2%. Most of the costs (66%) were attributable to falls in those aged ≥75 years. The major cost driver was inpatient admissions, accounting for 49.4% of total cost of falls. Long-term care costs were the second highest, accounting for 41%, primarily in those aged ≥75 years.
The research concluded that unintentional falls impose a substantial burden on health and social services. In 2000, the total cost was equivalent to almost 20% of the total NHS expenditure on pharmaceuticals, the total budget for 1 of 38 new strategic health authorities, or 3.3 times the total NHS funding earmarked for mental health, coronary heart disease, cancer, and primary care in England.
How many citations did the paper receive?
At the time of writing, Google Scholar results show that the publication has been cited over 770 times. Although the paper was published over 20 years ago, it has had 12 citations so far in 2026, and over 30 citations in 2025.
The paper has been cited in a large number of systematic reviews and meta-analyses, including topics such as tools for assessing fall risk in the elderly, the effects of physical exercise on balance in older people, and racial and ethnic differences in falls among older adults.
It has also been referenced in numerous cost-effectiveness studies; for example, looking at vitamin D therapy in unintentional falls, dose response resistance training in older women, and tramadol and the risk of fracture in an elderly female population.
Most recently, the paper has featured in studies looking at deep learning models and AI fall prediction tools for assessing the risk of future falls in older adults.
What wider impact has the paper had?
The publication has been influential in the creation and implementation of core outcome datasets in falls prevention research. In 2005, it was cited in the Development of a Common Outcome Data Set for Fall Injury Prevention Trials as a rationale for including the number of radiologically confirmed peripheral fracture events per person year as part of the outcomes in any future research.
In 2006, the paper was included in a review of social care by Sir Derek Wanless, commissioned by the King’s Fund. The review sought to determine how much should be spent on social care for older people in England and what funding arrangements needed to be in place to ensure that this money was available to produce high-quality outcomes. The paper was cited as part of the argument for funding “low-level” services, in order to make the case for falls prevention. Following on from this, the paper was again highlighted in the Handypersons Financial Benefits Toolkit, which outlined the potential benefits from funding a small repairs service and its ability to contribute to helping older people to stay independent in their own homes for longer.
Two years later, the World Health Organization (WHO) referred to the study in a global report on falls prevention in older age, which was translated into Italian, Korean and Japanese. It was included in a worldwide overview of the magnitude of falls. The report informed the development of the WHO Falls Prevention Model, an internationally recognised conceptual framework for falls prevention, which emphasises the assessment of individual and community risk determinants and the implementation of targeted interventions across individual, environmental, and health and social care settings.
The European Commission highlighted the publication in 2014 as the only European study on the costs associated with falls in a report on outcome indicators, which fed into the Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing (MAFEIP).
Has the paper been cited in guidance?
Given that the paper highlights the considerable risk factors of urinary infections as a cause of falls, it was cited in the Academy of Pelvic Health Physical Therapy clinical guidelines on Rehabilitation Interventions for Urgency Urinary Incontinence, Urinary Urgency, and/or Urinary Frequency in Adult Women. Addressing fall risk management was seen as best practice for health care providers in the guidance.
In 2011, Exercise and Sports Science Australia issued a position statement on exercise and falls prevention in older people. The paper sought to make the case that falls in older people are not random events but can be predicted by assessing a number of risk factors, some of which could be improved with appropriate exercise. The burden of falls was illustrated by inclusion of the paper in the review underpinning the position statement. The review was translated into practical exercise-prescription recommendations, particularly emphasising the role of challenging balance exercises.
Has the YHEC team worked on any similar topics?
A year earlier, Paul Scuffham and Rosa Legood had explored the cost burden of falls with Edward Wilson and Tessa Kennedy-Martin. This time, the team explored the incidence and cost of injurious falls associated with visual impairment in the UK, producing a prevalence-based cost model of medical care using national data for emergency department attendance and hospital inpatient admissions. The study estimated the medical costs of falls in visually impaired people were £269 million, and £128 million in cases where the visual impairment was directly responsible for the fall. 89% of these falls and the majority of costs occurred in those aged 75 years and over. The research reported that of the total cost of treating all accidental falls in the UK, 21% was spent on the population with visual impairment and 10% was directly attributable to visual impairment.
In 2018, YHEC were commissioned by Public Health England to develop an interactive, easy-to-use tool that could be used to estimate the cost-effectiveness and potential return on investment (ROI) for falls prevention programmes aimed at elderly people based in the community. The tool incorporated 4 programmes where there was evidence of cost-effectiveness: Otago home exercise, Falls Management Exercise group programme, Tai Chi group exercise, and home assessment and modification. Based on an example analysis undertaken in the York local authority population, all 4 interventions were found to be cost-effective, thus producing a positive societal ROI. The report, A Return on Investment Tool for the Assessment of Falls Prevention Programmes for Older People Living in the Community was later cited in a World Health Organization policy briefing on making the economic case for health promotion and disease prevention.
More recently, YHEC Principal Medical Writer, Emily Gregg, published a machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women. The aim of this cross-sectional study was to identify key variables that could best distinguish between older female fallers and non-fallers, using a sophisticated machine learning approach. The study found that it is possible to classify fallers and non-fallers with a high degree of accuracy using a combination of 18 variables from four domains, with the gait and strength domains being particularly informative for screening programmes aimed at assessing falls risk.
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References
- Scuffham P, Chaplin S, Legood R. Incidence and costs of unintentional falls in older people in the United Kingdom. J Epidemiol Community Health. 2003;57(9):740-744.
- Park SH. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2018;30(1):1-16
- Papalia GF, Papalia R, Diaz Balzani LA, et al. The Effects of Physical Exercise on Balance and Prevention of Falls in Older People: A Systematic Review and Meta-Analysis. J Clin Med. 2020;9(8):2595.
- Wehner-Hewson N, Watts P, Buscombe R, Bourne N, Hewson D. Racial and Ethnic Differences in Falls Among Older Adults: a Systematic Review and Meta-analysis. J Racial Ethn Health Disparities. 2022;9(6):2427-2440
- Poole CD, Smith J, Davies JS. Cost-effectiveness and budget impact of Empirical vitamin D therapy on unintentional falls in older adults in the UK. BMJ Open. 2015;5(9):e007910.
- Davis JC, Marra CA, Robertson MC, et al. Economic evaluation of dose-response resistance training in older women: a cost-effectiveness and cost-utility analysis. Osteoporos Int. 2011;22(5):1355-1366.
- Hirst A, Knight C, Hirst M, Dunlop W, Akehurst R. Tramadol and the risk of fracture in an elderly female population: a cost utility assessment with comparison to transdermal buprenorphine. Eur J Health Econ. 2016;17(2):217-227.
- Gao Y, Xu D, Li X, et al. Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence. J Med Internet Res. 2026;28:e84844.
- Lin FC, Chen PH, Yang CH, Yang CC, Chuang HY, Hung CH. AI-Driven Fall Prediction across Generations: Integrating Deep Learning and Machine Learning for Young, Middle-Aged, and Older Adults. Gerontology. 2026;72(1):3-18
- Lamb SE, Jørstad-Stein EC, Hauer K, Becker C; Prevention of Falls Network Europe and Outcomes Consensus Group. Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc. 2005;53(9):1618-1622.
- European Commission. Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing (MAFEIP) First report on outcome indicators. JRC Publications. 2014. Available from: https://publications.jrc.ec.europa.eu/repository/handle/JRC91162
- Wanless, D. Securing good care for older people: taking a long-term view. King’s Fund. 2006. Available from: https://www.kingsfund.org.uk/insight-and-analysis/reports/securing-good-care-older-people
- UK Government. Communities and Local Government. Handypersons financial benefits toolkit. 2010. Available from: https://assets.publishing.service.gov.uk/media/5a795ef9ed915d0422067e01/1514533.pdf
- World Health Organization. WHO global report on falls prevention in older age. 2008. Available from: https://iris.who.int/items/7580d84f-ee8c-4e5d-908f-0f2e44896395
- McAuley JA, Mahoney AT, Austin MM. Clinical Practice Guidelines: Rehabilitation Interventions for Urgency Urinary Incontinence, Urinary Urgency, and/or Urinary Frequency in Adult Women. Journal of Women’s and Pelvic Health Physical Therapy. 2023;47(4):214-216
- Tiedemann A, Sherrington C, Close JC, Lord SR; Exercise and Sports Science Australia. Exercise and Sports Science Australia position statement on exercise and falls prevention in older people. J Sci Med Sport. 2011;14(6):489-495
- Scuffham PA, Legood R, Wilson ECF, Kennedy-Martin T. The incidence and cost of injurious falls associated with visual impairment in the UK. Visual Impairment Research, 2002. 4(1), 1–14.
- Public Health England. A return on investment tool for the assessment of falls prevention programmes for older people living in the community. 2018. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/
679856/A_return_on_investment_tool_for_falls_prevention_programmes.pdf. - World Health Organization. Using economic evidence to help make the case for investing in health promotion and disease prevention. Health and Wealth Series (Tallinn Policy Brief 2). European Observatory on Health Systems and Policies. 2018. Available from: https://eurohealthobservatory.who.int/publications/i/using-economic-evidence-to-help-make-the-case-for-investing-in-health-promotion-and-disease-prevention-study.
- Gregg E, Beggs C, Bissas A, Nicholson G. A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women. PLoS One. 2023;18(10):e0293729.
