The impact of our ageing population: a Q&A with Carol Jagger

Written by Oz Flanagan on . Posted in Features

Humans are living longer than ever before, and with advances in medical technology, our life expectancy will only extend in the future. But a growing older population brings challenges for almost every aspect of a nation’s future planning, from the economy to healthcare.

Carol Jagger is a statistician and professor of epidemiology at Newcastle University’s Institute for Ageing. Her research focuses on the question of what effect an older population will have on the country’s health and social services.

However, at the heart of this question is how healthy and active future cohorts will be through their latter years. Quantifying this impact is very difficult, as people may be living longer, but these extra years can be hindered by (multiple) health complaints. This is why Carol’s work centres on tracking, and hopefully increasing, our healthy life expectancy.

We talked to Carol about her research and what the challenges the NHS will face in caring our ever ageing population.

Do you think society should place more emphasis on healthy life expectancy as opposed to simply living longer?

Yes I do. We can no longer assume that because life expectancy is increasing, this must mean we are healthier. Not least because life expectancy could be increased simply by improved medical technology keeping the frail and very old alive when in the past they would have died.

Many older people fear disability and dependency more than death. So we need to extend healthy life rather than just life. In 1997 the then director general of the World Health Organisation, Hiroshi Nakajima, said ‘Increased longevity without quality of life is an empty prize. Health expectancy is more important than life expectancy.’

In fact I think health expectancy and life expectancy must be monitored together with the aim that health expectancy should grow faster than life expectancy to ensure we do not increase unhealthy years.

What datasets are used to track the health problems of the older population and how do you statistically model this?

The main datasets I use are two longitudinal cohort studies: the MRC cognitive function and ageing studies (CFAS) and the Newcastle 85+ study. Both of these are total population studies, so include older people living in residential care. This is very important to get a true picture of some conditions like dementia.

Both have very good information on function, both mental and physical, which is a particular interest of mine. Another dataset is the English longitudinal study of ageing (ELSA) which doesn’t include older people in institutions but does follow them up if they are admitted.

In terms of modelling what might happen in the future, I am currently developing a microsimulation model to forecast trends in disability and associated conditions like cognitive impairment from 2010 to 2040. This will be based on CFAS (for those aged 65+), ELSA (for those aged 50-64) and the understanding society study (for those aged 35-49). This will ensure that we can say something about those aged 65 and over between 2010 and 2040. We’ll be including socio-demographic and lifestyle factors as well as diseases, and estimating the transitions between states of these factors, mainly from logistic regression models. 

We already have large gaps in life expectancy between richest and poorest parts of the UK. Do you expect this to get better or worse in the future?

The gaps in life expectancy across the UK are large. Across clinical commissioning groups for 2010-12, the male life expectancy gap at birth for Guilford versus Bradford City is 9.1 years. The female life expectancy gap at birth in Richmond versus Bradford City is 7.3 years, but they appear to have narrowed a little.

The gap in healthy life expectancy at birth (the average number of years in good health) also seems to have narrowed slightly, but the gap is around double that in life expectancy. For 2010-12, again in Guilford versus Bradford City, there is a gap of 17.8 years for males and 19.7 years for female healthy life expectancy at birth.

Unfortunately trying to understand the trends is tricky because the health and disability questions have changed over time. I’m not optimistic that inequalities will improve unless we make a concerted effort to engage communities to improve their lifestyles (smoking, alcohol use, obesity, physical activity) and thereby reduce the factors that we know are the main causes of ill-health and disability. 

At Newcastle a group of us are developing a healthy life simulation game which we have piloted a few times with different audiences – in schools, with a deprived community in Newcastle and with members of the local council. The game has two teams and the aim is to try to halve the gap in disability-free life expectancy at age 55 in ten years between an affluent area and a deprived one, but at no cost.

The simulation allows participants to explore different interventions (at an individual, community, local government or national government level) for disease prevention and health improvement and including how behaviour can best be changed. It has really made people widen their view on the possible solutions – but also that solutions that have a greater effect on mortality than disability, won’t reduce inequalities in disability-free life expectancy.

How profoundly do you think patients with multiple conditions will affect the NHS’s approach to healthcare?

I think that this is already happening. When the NHS was set up life expectancy at birth was around 65 years. Although people did live into their eighties and nineties, this was relatively rare. Indeed centenarians (people aged 100 years and over) numbered only 209 in 1946 whereas by 2000 there were 6,230 and by 2013 numbers had almost doubled to reach 11,610.

Since the prevalence of most of the major chronic diseases rises with age, it’s not surprising that by very old age multiple conditions are the norm. In our cohort of 85 year olds in Newcastle, men had an average of four conditions, and women five. Inevitably this means that the less life-threatening ones, though possibly the ones that affect quality of life the most like arthritis, get overlooked in a consultation.

More health problems means more medicines and therefore a greater likelihood that treatment for one condition might interfere with treatment for another. And since very old people with multiple conditions don’t get into clinical trials, there is little knowledge about how drugs might interact, particularly in a person who is more at risk of adverse events anyway. My view on this is that the NHS should be using the big patient databases to find the most common clusters of conditions, and then setting up clinics that deal with these clusters together – so that the patient is viewed holistically rather than as a set of organs.

Is it possible to pin down why another country does better or worse with life expectancy? Or is it statistically too complex a question to easily isolate?

It isn’t possible to say clearly why one country does better than another on either life expectancy or healthy life expectancy. Moreover, the countries that have the highest levels of life expectancy don’t always have the highest healthy life years.

One factor that we have found that does explain some of the inequalities in healthy life years across Europe is material deprivation. But generally we have to look at this by meta-regression because life tables are not available separately by education or deprivation. So we can’t directly compare life and healthy life expectancy for equivalent levels of other factors.

If we had longitudinal data that included mortality, then we could use estimate mortality by other socio-economic factors, but sadly these data aren’t available for all EU countries. Where longitudinal health data is available, for example in the survey of health and retirement in Europe (SHARE), mortality is not always completely ascertained.

How is the pensions and insurance industry taking note of this kind of statistical research on the older population?

The actuarial profession in general is becoming very interested in health expectancy. Of course what is of most interest is how health expectancy trends will play out in the future and this we do not know – not least because they trends are not as regular as say for life expectancy.

There are presently very few projection models for health expectancy. However, as I mention earlier, we are developing a microsimulation model as part of an ESRC funded project MODEM (Comprehensive approach to modelling outcome and cost impacts of interventions for dementia). This model will allow us to play out different scenarios for changing prevalence of disease and risk factors and see how we can compress disability. I think this will be of much interest to actuaries.

Another important aspect is in local areas where the disability-free life expectancy at age 50 is below 15 years. This means that the average age for the onset of disability is below the age of 65, and these areas will find it more difficult to keep older people in work for longer.

Moreover there is a strong geographical patterning to this with most local authorities in the North East and many in the North West and Yorkshire being in this situation. Meanwhile, relatively few local authorities in the South East and South West are.

National Health Service

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