Newcastle University Institute for Ageing – Written evidence (INQ0025)


Newcastle University Institute for Ageing[1], UK National Innovation Centre for Ageing2


We address our submission under the four headings in the Call for Evidence: Scientific Basis, Technologies, Industrial Strategy, and Healthier Ageing.


  1. Scientific Basis

1.1   The evidence for public health advice on the effect of healthier lifestyles on health is good but it is poor for healthy life expectancy (HLE). The main reason is that the majority of evaluations focus on health outcomes or mortality but not both, thereby enabling evaluation of effects on healthy life expectancy (health span) which is a composite measure of health and mortality. This is crucial because interventions that have a positive effect on health may also reduce mortality and therefore the gain is not solely in health but also in length of healthy life years. Evidence from cross-sectional studies suggests that, of the three lifestyle factors assessed: smoking, obesity and alcohol consumption, obesity is most strongly associated with spending more years of life with disability, with years lived with disability from age 55 differing by 2.8 years according to BMI, 0.2 years by smoking and 1.6 by alcohol consumption (Klijs et al. 2011). In contrast, smoking has the strongest effect on life expectancy. Obese persons could expect to live more years with disability (5.9 years) than smokers (3.8 years) and drinkers (3.1 years). Longitudinal observational studies report similar findings (Majer et al. 2011).

1.2   In addition to health behaviours, inequalities in HLE and Disability-free Life Expectancy (DFLE) are evident across a number of groups defined by socio-demographic factors such as socio-economic status (SES), particularly education, race/ethnicity and gender (Pongiglione et al. 2015). The degree to which health behaviours mediate education inequalities in HLE or DFLE has not been determined.

1.3   A scoping review of inequalities, conducted for the Centre for Ageing Better, identified robust evidence concerning the association between a range of socioeconomic factors and poor physical and mental health outcomes in later life in England ( Evidence of the social patterning of inequalities across the life course, leading to stark differences in HLE and DFLE, points in a consistent direction, with people of lower socio-economic status having poorer outcomes than those of higher socio-economic status. Individual characteristics, such as age, sex, ethnicity, and socioeconomic circumstances, may intersect with area of residence to contribute to especially poor health outcomes for some groups in later life (Scharf et al. 2017).

1.4   Furthermore, wider environmental factors play a crucial role in the health and well-being of older people (Centre for Ageing Better 2019). Issues such as housing, transport, fuel poverty, and access to resources are pertinent for older populations – these offer key avenues for prevention as highlighted in recent report by the Association of Directors’ of Public Health (see A whole systems approach needs to be adopted to support older adults to live independent healthy lives – this would mean adopting place-based approaches that promote integration between local authorities, health and community partners.

1.5   The differences in DFLE between ethnic groups is particularly concerning given that ethnic minorities will be entering the older population in larger numbers in the coming decades (Wohland et al. 2015). The degree to which these differences are a result of lifestyle, SES or environment are unknown, and updated estimates of differences are needed although this is difficult as life tables are not yet routinely available by ethnic group.

1.6   The INNOVAGE project undertook a global search for relevant examples of social innovations, and then established a system to compare social innovations in terms of their potential impact on the European population indicator Healthy Life Years (HLY) (see Of the 65 exemplar social innovations identified, the majority of their evaluations were essentially of process measures or proximal outcome measures, rather than the outcome of the target (see Only one evaluation, of preventive home visits on functional decline (Vass et al. 2005), incorporated both functional decline and mortality as outcomes, therefore allowing estimation of the effect of the social innovation on disability-free life expectancy.

1.7   Many social innovations, with potential to impact positively on active and healthy ageing, are developed by community groups, or others with little background in the evaluation of interventions. The NHS Long Term Plan (see does recognise the value of such innovations with, for example, its investment in link worker social prescribing to patients to local community groups and support services. Randomized controlled trials are generally not appropriate in these settings but there are before-and-after designs that are appropriate. Some robust evaluation is necessary to enable an understanding of ‘what works’; one example is a mixed methods evaluation of a social prescribing intervention combining quasi-experimental methods and qualitative observation (Moffatt et al. 2019). A relatively simple solution is to include in evaluations a simple global health measure, for example self-rated health or the Global Activity Limitation Indicator (Berger et al. 2015), along with mortality, that would enable comparison between interventions to establish which would have the greatest impact. There is also the potential in the future for the real-time capture of the performance of activities early in the functional decline framework in order to flag up the onset of difficulty and address these early to restore function or slow down further decline (Gore et al. 2018).

1.8   As noted in 1.1-1.5 there is substantial evidence, from individual studies and systematic reviews, of the behavioural, social and environmental determinants of health, rather than healthy life expectancy. In terms of demographic differences, most studies report that women live longer but have an earlier onset of disability and live a greater proportion of their remaining life with disability.


  1. Technologies

2.1   Harnessing innovation, in service delivery, products and process will be key to supporting healthy and independent living. Innovation in design, technology and services is needed to deliver meaningful and visible products that are aspirational rather than ‘beige and boring’. Identification of need and subsequent co-design with the end user through mechanisms such as VOICE (, part of the UK’s National Innovation Centre for Ageing (NICA), which offers an approach for industry to de-risk investment in the ‘longevity’ or ‘age-tech’ market.

2.2   The UK Longevity Council is currently being established to advise on how best to use innovations in technology, products and services to improve the lives of the ageing population.

2.3   Online health resources in ageing have potential to generate adverse consequences for older people. Thus far, online GP services have served a self-selecting patient population who are younger, healthier and more affluent than average.  Selective removal of healthy, working age patients from traditional GP lists, will increase the proportion of older people with multiple conditions and complex care needs amongst those who remain.  This has potential to destabilise fragile health care economies.


  1. Industrial strategy

3.1   An important element of the Industrial Strategy is the framing of mission-oriented approaches to addressing the grand challenge of an ageing society as described in A Mission-Oriented UK Industrial Strategy’ from the UCL Commission for Mission-Oriented Innovation and Industrial Strategy (MOIIS) (available at The overarching Government mission “to ensure that people can enjoy at least five extra healthy, independent years by 2035, while narrowing the gap between the experience of the richest and poorest” was motivated in part by a previous simulation exercise conducted by Newcastle University, setting a mission to reduce by half the social gradient in healthy life expectancy at age 55 within 10 years at no extra public cost. The simulation was played out by teams combining expertise from Health and Wellbeing Boards and citizens, informed by a comprehensive model that incorporated a wide range of data on impacts of lifestyle and other interventions. The simulation proved to be a powerful tool to explore the results of actions selected by the teams and it has served also as a framework to engage and inform a variety of stakeholder groups. The MOIIS report provides details of other potential missions relevant for the ageing aspects of the Industrial Strategy, for which similar simulation methodologies might provide early and cost-effective ways to assess the complex effects of specific initiatives. 

3.2   Importantly, the Industrial Strategy also recognises the economic opportunities emerging as a consequence of a (globally) ageing population. The ‘longevity’ market is significant – e.g. by 2020, consumers aged 60+ will spend $15 trillion p.a. globally. The UK can gain comparative advantage by acting first or early in these longevity markets. The UK Industrial Strategy provides a stimulus and framework for action through the Ageing Grand Challenge, as well as associated working groups such as the Ageing Grand Challenge International Forum, and the joint establishment by UK Government and Newcastle University of the National Innovation Centre for Ageing.


  1. Healthier ageing

4.1   The latest estimates of DFLE[2] at birth for England (2015-17) are 63.1 years (males) and 62.2 years (females) (Office for National Statistics 2018). An increase of five healthy years corresponds to an increase of 8% for both male and female DFLE, which, at age 65, would correspond to an increase from 9.9 years to 10.7 years (males) and from 9.9 years to 10.6 years (females), thus an increase of 0.8 years disability-free at age 65. Between 1991 and 2011 years independent at age 65 increased by 1.7 years for men (from 9.5 to 11.2 years) but for women by only 0.2 years (from 9.5 to 9.7 years) (Kingston et al. 2017).  The Population Ageing and Care Simulation (PACSim) model forecasts that, between 2015 and 2035, years spent independent from age 65 will increase by 4.2 years for men and 0.9 years for women (Kingston et al. 2018a). For men life expectancy at age 65 will increase by less than life expectancy independent and so there will be a compression of dependency for men; this will not be true for women where there will be an increase of dependency, albeit low level dependency requiring less care than daily. Larger increases in DFLE at age 65 for men than women are also forecast from 2015 to 2025 by another microsimulation model but based only on ELSA rather than the three studies underlying PACSim (Guzman-Castillo et al. 2017).

4.2   PACSim also forecasts considerable increases in the prevalence of, and years spent with, multimorbidity (Kingston et al. 2018b). Reasons for this are two-fold, the rise in the numbers of the very old, where multimorbidity is common, but also a greater likelihood of already having one disease for those entering the older population in subsequent years. There will therefore need to be a greater focus on prevention in early and mid adulthood, as well as a greater focus on delaying the disabling consequences of chronic conditions.

4.3   Although the above suggests that an increase in five healthy life years may be achievable by 2035, estimates are based on the 2014 population projections, and, more recently, life expectancy has stalled. Moreover, evidence on how inequalities in DFLE will play out is lacking.

4.4   In 2008, the European Innovation Partnership on Active and Healthy Ageing (EIP-AHA) set a target of an increase of two HLYs at birth for the EU by 2020. Estimates of HLY at birth from Eurostat to 2017 show that the target has already been exceeded by men and has almost been reached by women (Table 1). However, HLY gaps between the EU28 Member States have widened further (Table 1), as predicted from different scenarios to achieve the target (Jagger et al. 2013).

4.5   Evidence from the EU scenarios above demonstrated that to reach the EIP-AHA target by 2020 for all countries and reduce the inequalities between Member States, HLY in the EU27 would have to rise by 4.6 years to achieve a 30% reduction in the gap between Member States, and 6.4 years to achieve a 50% reduction. However, reducing the gap by 50% alone would result in all but two Member States increasing their HLY by two years or more, the two Member States being Malta and Sweden who already enjoy the longest HLY in the EU. These results are particularly pertinent to the UK since the target involves a larger increase in healthy years (albeit over a slightly longer time frame), but also a commitment to reduce inequalities that are already very large and increasing (currently over 18 healthy life years at birth between the most and least deprived areas) (Office for National Statistics 2019). Thus evidence from the EU suggests that to reduce inequalities the overall increase in healthy life expectancy may need to be greater than five years.

4.6   A greater use of microsimulation modelling that incorporates the health of younger people who will attain old age by 2035, as well as what-if scenarios from the likely effect of healthy lifestyles, slowing down of functional decline (Gore et al. 2018), and the reduction of inequalities, would enable a better understanding of whether and how the Government target can be achieved.


Table 1: Healthy Life Years (HLY) at birth for EU28 Member States in 2008 and 2017, and size of gap in HLY between Member States, by gender (Source: Eurostat)





Difference (2017-2008)

HLY at birth (years)









Gap in HLY  at birth (years)











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Centre for Ageing Better (2019) The State of Ageing in 2019: Adding life to our years. Centre for Ageing Better, London. doi:

Gore PG, Kingston A, Johnson GR, Kirkwood TBL, Jagger C (2018) New horizons in the compression of functional decline. Age Ageing 47 (6):764-768.

Guzman-Castillo M, Ahmadi-Abhari S, Bandosz P, Capewell S, Steptoe A, Singh-Manoux A, Kivimaki M, Shipley MJ, Brunner EJ, O'Flaherty M (2017) Forecasted trends in disability and life expectancy in England and Wales up to 2025: a modelling study. The Lancet Public Health 2 (7):e307-e313.

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Kingston A, Robinson L, Booth H, Knapp M, Jagger C, Modem project (2018b) Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model. Age and Ageing 47 (3):374-380.

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Office for National Statistics (2018) Health state life expectancies, UK: 2015 to 2017. doi:

Office for National Statistics (2019) Health state life expectancies by national deprivation deciles, England and Wales: 2015 to 2017. doi:

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Scharf T, Beach B, Hochlaf D, Shaw C, Bamford SM (2017) Scoping Review of Inequalities in Later Life. London. doi:

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18 September 2019



[1] Dame Louise Robinson, Director Newcastle University Institute for Ageing and Regius Professor of Ageing

Carol Jagger, AXA Professor of Epidemiology of Ageing

Clare Abley, Nurse Consultant Vulnerable Older Adults & Honorary Clinical Senior Lecturer

Lynne Corner, Director of VOICE and Professor of Engagement

Barbara Hanratty, Professor of Primary Care & Public Health

Andrew Kingston, Research Fellow in the Epidemiology of Ageing

Thomas BL Kirkwood, Emeritus Professor

Fiona Matthews, Professor of Epidemiology

Suzanne Moffatt, Reader in Social Gerontology

Sheena Ramsay, Clinical Senior Lecturer & Honorary Consultant in Public Health

Thomas Scharf, Professor of Social Gerontology

2 Patrick Bonnett, Director, UK National Innovation Centre for Ageing, Newcastle University

[2] Disability-free life expectancy (DFLE) from the Office for National Statistics estimates lifetime free from a limiting persistent illness or disability, based upon a self-rated assessment of how health conditions and illnesses reduce an individual’s ability to carry out day-to-day activities