Better policy for healthy lives

Find out more about our work to promote policies that support everyone’s opportunities for a healthy life

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Change can be achieved by recognising that wellbeing and health need to be an outcome for all future policies implemented in the UK. National and local government must adopt policies that focus on the long-term prevention of ill health.

Policy recommendations for young people's future health

Article
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15 October 2019

Read about the nine expert organisations selected to provide a deep dive into seven key policy areas affecting young people's future health.

Advancing our health: prevention in the 2020s

Consultation response
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October 2019

Health Foundation response to the Cabinet Office and Department of Health and Social Care Advancing our health: prevention in the 2020s consultation.

Government needs to address root causes of obesity

Press release
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10 October 2019

Our response to the Chief Medical Officer’s report on childhood obesity

Harnessing data and technology for public health: five challenges

Responding to the government’s prevention green paper

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Introduction

In this long read, we set out five challenges that the government needs to address if it is to harness the full potential of data and technology in public health, and offer a suggestion to help address each.

Technology and data have revolutionised so many areas of our lives, it is natural to ask what they can do for public health and prevention. Public health professionals have always used data to understand the health of populations and galvanise change as a core part of their approach. 

But today, some people feel that the scale of new data sources, advances in genetic sequencing, machine learning and artificial intelligence (AI) could usher in a new era of more personalised interventions underpinned by more precise risk prediction. The same data and technology have the potential not just to achieve greater personalisation but also to transform our understanding of the risks facing populations so that we can take action.

Given the interest in this area, and recent announcements of large-scale NHS investment in AI, it is unsurprising that better use of data and technology are central themes for the government’s prevention green paper Advancing our health: prevention in the 2020s.

The green paper presents the use of technology and data as a major opportunity for improving public health in future. It advocates a ‘new prevention model’ that is ‘proactive, predictive and personalised’, with a vision of 'a new wave of intelligent public health where everyone has access to their health information and many more health interventions are personalised'. See page 3

Specific proposals include greater use of genomics in health care, a review of the NHS Health Check and the first phase of a ‘predictive prevention’ work programme by Public Health England and NHSX.  

The predictive prevention programme is intended to evaluate and model predictive prevention at scale, working on two key areas: 

  • developing exemplar projects to prove the concept of personalised prevention and establish the evidence base 
  • designing the future shape of the programme, with a view to increasing the scale and ambition.

In this long read, we set out five challenges that the government needs to address if it is to harness the full potential of data and technology in public health, and offer a suggestion to help address each.
The challenges are:

  1. balancing interventions that reduce individual susceptibility versus interventions that tackle the underlying causes
  2. balancing universal interventions against targeted approaches
  3. making prevention services accessible to those who need them most
  4. closing the evidence gap between prediction and prevention
  5. balancing investment in novel solutions against funding tried-and-tested solutions.

The hope, set out in the green paper, is that new, ‘smarter’ approaches to prevention will help address entrenched problems (such as health inequalities) and worrying trends (such as the stalled improvements in life expectancy). However, if real progress is going to be made in improving the public’s health, history tells us that some fundamental tensions in public health policy and practice need to be addressed.

1. Reduce individual risk or tackle underlying causes?

Organised approaches to improving public health have always combined two types of intervention:  

  • those designed to reduce the risk faced by individual people 
  • those targeting underlying causes of incidence in populations. 

There are good reasons – both theoretical and empirical – to believe that, while both approaches are needed, those that address the underlying causes have the biggest impact on population health. By focusing on ‘personalisation', the green paper’s vision for data and technology falls into the former category, limiting its potential impact.

It can be helpful to distinguish between the underlying causes of the rate of an illness in a population and the factors that determine which individuals are affected by that illness. 

For example, in the 19th century the level of cholera was driven by factors such as availability of clean water, sanitation and population density. Which individuals were most affected within the population, however, was determined by a different set of risk factors, such where they lived, which water source they used, and how they used water.  

Epidemiologist Geoffrey Rose explored these different types of causation in his 1985 seminal paper on public health strategy, Sick individuals and sick populations. Rose described two distinct drivers of illness: 

  • determinants of incidence rate (the underlying causes of disease) 
  • determinants of individual cases (or individual susceptibility). 

These drivers can be addressed through two corresponding public health strategies:

  1. the population approach, which addresses the causes of incidence
  2. the high-risk approach, which tries to protect susceptible individuals.

Since the beginning of the discipline, both of these strategies have been employed as part of an effective public health approach.  

Rose concluded that: ‘The two approaches are not usually in competition, but the prior concern should always be to discover and control the causes of incidence.’ In other words, priority should be given to the underlying causes in populations because the vast majority of cases of illness occur in individuals at moderate risk rather than those at the extreme end of the spectrum. Thus, shifting the curve by a small amount will have much bigger impact on health at population level than focusing on those at highest risk.

There may be some gains to be made in reducing risk to individuals, but there is a real danger that this focus promotes a reductive model of public health that focuses on individual behaviour rather than the wider (social, economic, environmental and commercial) determinants.

Here it’s useful to consider how we tackled the big health problems in the past. Victorian reformers and innovators brought clean water, improved housing and sanitation to Britain’s cities. Transmission of infectious diseases such as cholera was eliminated through major reforms that tackled structural issues – including through novel uses of data such as cutting-edge approaches to mapping and visualisation.

As well as improving health generally, this focus on underlying causes reduced health inequalities. This is because it brought far greater benefit to those communities that were more deprived, as it was they who had borne the brunt of poor water quality and overcrowding. Reformers did also promote individual-level approaches, such as sharing good hygiene practices (such as handwashing, boiling water and using antiseptics). These will have benefited many people at an individual level, but with a much smaller impact on overall population health and inequalities.

Today’s major public health challenges may appear more complex, but we can apply similar principles. There is abundant evidence that the strongest drivers of population health and health inequalities are not individual-level factors but structural issues such as income, education, housing and clean air: the wider determinants of health. These influence the health of populations powerfully – not only because they affect whole population groups but because they are ‘the causes of the causes’: that is, they strongly influence individual-level risk factors.

Take obesity, for example. Supporting individual behaviour change (for example, through weight management programmes) may be of real benefit to individuals. But evidence suggests that major population-level changes are only likely to happen when the structural determinants that create an obesogenic environment are also tackled effectively. For example, the levy on manufacturers has incentivised a 28.8% reduction in sugar content of drinks affected by the levy.

The sugar levy can be seen as an effective measure to address an underlying cause of incidence. However, even this measure on its own has not been enough to reduce significantly the sugar content of people’s total diets. This is because their average intake of sugar from foods that are not subject to an industry levy has increased. So, reducing exposure to unhealthy food and drink products has potential to have significant population-level impact but requires a wide-ranging, whole-system approach.  

Conflating risk prediction with personalised prevention

In the green paper, the data and technology solutions focus on addressing individual susceptibility – especially by giving people with information and personalised intervention. Indeed, this emphasis is so strong that the green paper risks conflating risk prediction with personalised prevention entirely. 

However, to make a real impact on population health and health inequalities it will be necessary to apply data and technology to the wider determinants of health

This could include, for example, investing in local public health teams’ ability to understand and track changes in the wider determinants in their population. New streams of data on issues such as air pollution, accessibility of green spaces, data from across government services, and acute problems such as drug use or changes in the labour market are becoming easier to collect at scale, and could all be useful.

Similarly, new data could help give local and national policymakers a more sophisticated understanding of commercial determinants, such as the availability and pricing of food and drink. Meanwhile, there are a number of emerging public health concerns that are fundamentally digital issues, such as online gambling, the impact of social media on mental health, and vaccine hesitancy. These, too, are likely to need population-level digital solutions.

Focusing on individual susceptibility may seem like a quicker and easier solution than addressing underlying structural issues. Policymakers are often understandably attracted to interventions that are relatively easy to implement and likely to have a short-term impact. However, if it is to make significant long-term improvements in health the government needs a sophisticated understanding of wider determinants. And making the most of these opportunities requires investment in technical tools (for data collection, dissemination and analysis) as well as in the skills needed for data analysis – which is extremely challenging given the cuts to the public health grant in recent years.

The government needs to focus on what will have the biggest impact on population health, addressing wider determinants of health, not just individual-level susceptibility.

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2. Universal versus targeted interventions

A key problem in prevention work is ensuring that interventions reach those who are most likely to benefit. This issue is less about what causes to target and more about how wide to cast the net. Many see better targeting of prevention interventions as one of the main benefits of introducing new data and technology to public health and prevention, but there are challenges with this approach.

Screening programmes

In one example, the green paper proposes ‘reviewing the NHS Health Check and setting out a bold future vision for NHS screening’ to make it more targeted. This is because, while screening programmes make great intuitive sense and are generally popular with the public, balancing their costs and benefits is complex, and there is clear evidence that, if applied inappropriately, screening can do more harm than good. 

The decision to overhaul NHS health checks is welcome given the limited evidence for their effectiveness and cost-effectiveness. But finding a more effective way of using health checks will need investment and careful consideration of the costs and benefits.

While the evidence on more targeted health checks is yet to be developed, there is some evidence for other forms of screening. For example, a modelling study on breast-cancer screening suggests that moving from the current age-based screening strategy to a risk-based approach could improve cost-effectiveness, reduce overdiagnosis and maintain the benefits of the current screening programme. 

Risk for health inequalities

But a key challenge for greater targeting of prevention interventions will be their impact on health inequalities. For example, the current NHS Health Check programme is effectively universal for 40–74 year olds. However, as people who take up health checks tend to be healthier than the general population, the programme brings more benefit to people who had better health in the first place.

It is possible that technology and data may be able to help with these problems. For example, linked data could be used to target and monitor the impact of health checks in marginalised groups. This on its own may not address inequalities in access but could be used alongside tailoring, community engagement and behavioural science approaches to ensure that people who are currently missing benefit more in future. 

Approaches of this kind, however, need to be tested. If risk prediction is to be used to make prevention strategies more targeted, this approach must be based on robust evaluation of costs and benefits, and must be designed to ensure that they have a positive, not a negative, impact on health inequalities. 

The government needs to find the right balance between universal versus targeted interventions. There may be some benefits to greater targeting of existing services, such as screening, but the costs and benefits need to be weighed carefully and evaluated robustly.

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3. Ensuring access for all

As we saw in the last section, the groups that are targeted (those to whom the services are offered) are not always the people who ultimately benefit from them. Health checks that are offered to the whole population often rely on individuals engaging first with the check itself and then with follow-up health promotion initiatives.

This is inequitable because people living in socially and economically deprived circumstances can find it more challenging to make use of these initiatives. For example, people living in more deprived areas or with fewer social connections are less likely to participate in preventive health services including some cancer screening programmes and routine health checks.  

In this area, technology offers opportunities but also challenges, which we explore through two examples.

Switching services to digital challenges

Moving services to digital channels could expand access to care, reduce stigma of accessing them, or help people fit them in around work and family commitments. However, this also raises the risk of digital exclusion. The Good Things Foundation estimates that 7.8 million people in the UK never use the internet and a further 7.4 million people use it infrequently and may find more advanced uses of the internet challenging. Older people, those living with disability or chronic illness, and those with lower levels of education or on a low income are more likely to be non-users or limited users. Technological solutions that depend on users having sufficient skills to access them may exclude many of those who are most in need.

Using data to target high-need groups

Another use for data-driven technology could be to identify and target those who are not currently reached by prevention interventions. If initiatives can use linked data to target and monitor the impact of the health checks in marginalised groups, this could help reach the people who are currently missing out. 

However, there is a problem with the data systems that could be used to do this: they often reflect the structural inequalities in our society. For example, many risk-prediction innovations are based on genetic datasets that have historically excluded many populations. People of European ancestry make up 79% of all participants of genetic studies. Steps are being taken to improve this, but tests available today may still exclude certain groups. 

Biases in data

Similarly, even in a health system that provides universal access, like the NHS, electronic health records will contain biases. For example, some groups may be less likely to seek treatment, or may receive a different diagnosis if they do. So, their needs will not be captured in the data. So, algorithms can perpetuate or reinforce existing biases in risk assessment processes.

There is a real risk that the people who are least able to access appropriate health care are the most likely to be under-represented in data sources. So, balancing the benefits of data and technology solutions against the risk that they will exclude some populations is a major challenge.  

It is important to apply data and tech solutions in ways that reduce health inequalities and benefit those most in need. This includes addressing explicitly the impact of new solutions on health inequalities, taking into account the barriers that some groups face in accessing preventative services.

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4. The prediction–prevention gap

Having assessed who is at greatest risk, the next challenge for public health is to convert this knowledge into effective strategies to prevent illness. Understanding risk is important but, as the history of tobacco control shows, using that information to produce better population health can be surprisingly difficult. 

More accurate, and more personalised, risk prediction is one of the main areas that the green paper highlights as an application of data and technology for prevention. It is true that linking large datasets, using advanced analytical techniques and incorporating genetic information, promises more granular prediction of risk than ever before. At the same time, information technologies provide more channels than ever for getting that information to individuals and health care professionals.

What is less clear, however, is how all this will help improve people’s health. It isn’t yet clear how new risk prediction from genetics and other data sources can be effectively deployed as part of a prevention strategy. At present, there is a prediction–prevention evidence gap.

Risk information for behaviour change

One approach promoted in the green paper is to give individuals more information about their risk of disease, in order to motivate behaviour change. However, existing evidence suggests that simply giving people more risk information has little or no effect on their behaviour.

A systematic review by French et al found that communicating disease risk information to patients – even when highly personalised – does not produce strong effects on smoking, physical activity, diet or alcohol consumption. Similarly, another systematic review found that communicating genetic risk information had no impact on a range of health-related behaviours. 

Further evidence on giving health care professionals and patients risk estimates for cardiovascular disease and cancer found that, although it might increase the accuracy of their risk perception, it had little effect on health-related behaviour.

Data to personalise interventions

A second approach that it promotes is to provide new information to clinicians or public health professionals, to help them target individuals for intervention, or to tailor a programme of preventive care. This approach is already used by the NHS, which applies risk-prediction tools to help clinicians identify and refer patients and to segment the population when designing and delivering care pathways.

Like screening, this approach seems intuitively advantageous but may not always be effective in practice. One example was the PRISM (Predictive Risk Stratification Model) system, which provided information to GPs in Wales about which patients were at highest risk of hospital admission. Trials found that use of this tool was associated with an increase in hospital admissions of around 3% over 12 months, but without clear benefits to patients. 

A further issue is that risk prediction can only be converted into better outcomes if other factors are in place – for example, in this case, if GPs had access to the right preventive community services. If better risk prediction is to be converted into more effective prevention, this depends on a wide range of factors, both inside and outside the health system.

It is too simplistic, therefore, to assume that what Public Health England describes as ‘sorting people into more precise groups according to their risk for common conditions’ will automatically improve health and prevent disease. To make the most of the opportunities, the government needs to address the gap between prediction and prevention and apply the predictive capabilities of new technologies alongside proven interventions, as we explore in the next section.

It is essential to address the prediction–prevention evidence gap. More granular calculation of risk doesn’t automatically translate into more effective prevention or improved health. Robust research and evaluation is needed to develop the evidence base bridging this gap.

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5. Novel solutions versus tried-and-tested ones

The green paper emphasises the potential of new technological to improve prevention. But it is important to balance this against the many existing interventions that have a proven track record.

Established interventions

There is strong evidence for the effectiveness and cost-effectiveness of a wide range of preventative interventions that are currently under-funded, ranging from smoking cessation to Sure Start Centres. Decisions about investing in new technologies must be made in the context of what we already known about the value of existing interventions that could be deployed with no further evaluation. Investment decisions also need to take into account the impact of novel interventions on equity.

Similarly, there are established methods for comparing the cost-effectiveness of existing interventions. In comparison, it is less clear how to make rational decisions about how much resource (including time and money) to invest in developing novel solutions for public health problems. Seizing promising opportunities for the future while avoiding the potential to be carried away by hype requires complex judgements to be made.

Proportional investment

A key challenge is to ensure that any investment is proportionate. Understanding the potential population-level impact of new data and technology approaches to prevention could help with this. 

This strategy is already used for national screening and vaccination programmes, with simulation models assessing the potential impact of new interventions (or ways of deploying existing interventions, such as risk-based screening strategies). Similar modelling can be used in public health, to inform the use of new predictive prevention techniques. 

It will be important that new interventions are compared not only against ‘do nothing’ scenarios but against existing prevention interventions that have a strong evidence base. This will allow informed decisions to be made about where to invest limited resources. 

Another way of ensuring appropriate levels of investment is to ensure that the impact of new technologies is evaluated robustly and rapidly, to demonstrate effectiveness and refine quickly for improvement. Without this, there is a high risk of scarce resources being put into a programme that is not effective or cost-effective (as happened with NHS Health Checks) due to a lack of robust evaluation and research.

It is important to balance investment in novel technological solutions with the need for ongoing investment (or reinvestment) in tried-and-tested prevention methods. Effective use of modelling methods and robust, rapid evaluation can help ensure that scarce resources are not poured into solutions that have little or no impact on population health. 

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Conclusion

There is clearly real potential for data and technology to help keep people healthy and prevent illness. Indeed, this is already happening. But there are also dangers, such as the potential for novel solutions to increase health inequalities, and there are real opportunity costs. 

This document sets out the following five challenges with taking forward the vision for data and technology, as set out in the green paper:

  • The government needs to focus on what will have the biggest impact on population health, addressing wider determinants of health, not just individual-level susceptibility.
  • The government needs to find the right balance between universal versus targeted interventions. There may be some benefits to greater targeting of existing services, such as screening, but the costs and benefits need to be weighed carefully and evaluated robustly.
  • It is important to apply data and tech solutions in ways that reduce health inequalities and benefit those most in need. This includes addressing explicitly the impact of new solutions on health inequalities, taking into account the barriers that some groups face in accessing preventative services.
  • It is essential to address the prediction–prevention evidence gap. More granular calculation of risk doesn’t automatically translate into more effective prevention or improved health. Robust research and evaluation is needed to develop the evidence base bridging this gap. 
  • It is important to balance investment in novel technological solutions with the need for ongoing investment (or reinvestment) in tried-and-tested prevention methods. Effective use of modelling methods and robust, rapid evaluation can help ensure that scarce resources are not poured into solutions that have little or no impact on population health. 

These challenges must be addressed if new data and technology solutions are to have a real impact on population health. If we are to make the most of the opportunities, the government’s vision of new data and technology for public health must move beyond personalisation and consider the wider potential to improve the public’s health.  

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Further reading

Consultation response

Advancing our health: prevention in the 2020s

Health Foundation response to the Cabinet Office and Department of Health and Social Care Advancing our health: prevention in...

Long read

A health index for England: opportunities and challenges

About 15 mins to read

This long read explores the government’s proposal to place health as a core measure of government success by creating a natio...

Analysis

Creating healthy lives

This publication makes the case for an ambitious, whole-government approach to long-term investment in the nation’s health. I...

Comms strategy
Content sub-type

A health index for England: opportunities and challenges

Responding to the government’s prevention green paper

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Introduction

In this long read, we explore the government’s proposal to make health a core measure of government success by creating a national health index for England.

We consider how a national measure of health might be constructed, what it could include, and how it could be embedded in government decision making. 

The surge of action on climate breakdown across the world in recent months is a reminder that a short-term focus on economic growth can have long-term consequences for the planet and humanity. While industrialisation has brought huge benefits, it has also created unintended consequences that could be disastrous. What’s the value of higher GDP if it comes at the cost of human health and wellbeing?  

The limitations of using GDP as a primary measure of national success have been discussed for many years, but it wasn’t until this year that one country – New Zealand – took the bold step of moving beyond GDP through its wellbeing budget. Now governments of other countries, including the UK, are questioning whether this kind of approach could be right for them too.

Green paper

The government’s recent green paper Advancing our health: prevention in the 2020s proposes the creation of a new health index for England, ‘to help us track the health of the nation, alongside other top-level indicators like GDP.’

The idea, first aired in the Chief Medical Officer’s 2018 annual report, has divided opinion. Some would like to see a measure of the nation’s health as a top-level national indicator, while others feel that we already have more than enough data on health to guide policymaking and that new measures will help only if they drive policy in a meaningful way.

We will: launch a new health index to help us track the health of the nation, alongside other top-level indicators like GDP. 
See page 5

As recommended by the Chief Medical Officer for England in her 2018 Annual Report, we will develop and launch a new Composite Health Index. The Index will provide a visible, top-level indicator of health, and can be tracked alongside our nation's GDP. It will measure changes in health over time and, along with other indicators, can be used by the government to assess the health impacts of wider policies. This is part of a broader shift towards viewing health as one of the primary assets of our nation, contributing both to the economy and to the happiness of the population.
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  • We welcome the recognition of health as one of the primary assets of our nation and support the central proposal that the nation’s health should be considered a key measure of national success.
  • To assess the complex nature of health, a suite of measures would be more effective than a single composite measure. However, there is a case for a single headline measure of health – such as the share of the population with good health – supported by a suite of measures that provide more detail to guide decision making.
  • If a new measure of health is to make a real difference, it will need to be supported by structures that embed health impacts with key decision making processes and have strong cross-party and public support.

1. Why develop a new national health measure?

The green paper proposes a new index ‘as part of a broader shift towards viewing health as one of the primary assets of our nation, contributing both to the economy and to the happiness of the population.’ The Health Foundation supports the idea of putting health measures at the centre of national policy, but have concerns about the proposed approach.

Health as an asset

At the Health Foundation, we have championed the concept of health as a national asset, and this forms the cornerstone of our healthy lives strategy. This framing acknowledges that good health is not only an important value in its own right but an essential foundation for a flourishing economy and society. It considers health not solely as an individual attribute but, when considered across a whole population, as a ‘stock’ that can either rise or fall depending on the circumstances that people experience.  

Ideally, policymaking across the whole of government (including early years development, education, housing and good quality work) would be designed to improve ‘health stock’ by creating the conditions for people to lead healthy lives. However, the long-term nature of the investment required means that short-term competing interests often divert away the time and attention needed. 

Improving health

Using overarching measures of health across government to track the impact of policy might help tackle this, by making it easier to consider people’s health as an outcome of policy (in other words, a key measure of success) as well as an input (an important prerequisite for achieving other outcomes, such as economic growth).  

Viewing people’s health as an asset could encourage policymakers to prioritise actions that create the right conditions for healthy lives, tipping the balance in favour of the long-term investment and policy action required.  

In recent years, the UK has seen disinvestment in many of the things that keep people healthy, such as public health, welfare, preventative children’s services. At the same time, an increasing amount of government spending has been poured into reactive services, to mop up avoidable problems in areas such as health care, temporary housing and children’s services.  

Similarly, other areas of government policy – from taxation of food and drink to economic development strategies – have long-term consequences for health and are at risk of being overly influenced by short-term political pressures. 

Advocates for a national health index see it as a means of rebalancing government investment (of financial and political resources) away from short-term fixes and towards long-term health creation.  

Viewing people’s health as an asset could encourage policymakers to prioritise actions that create the right conditions for healthy lives, tipping the balance in favour of the long-term investment and policy action required.

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2. What would a new health measure look like – and what are the potential problems?

The green paper suggests that the national health index should be a composite. This type of measure combines several different indicators into a single score. This makes them complicated to construct but easy to read. In the Chief Medical Officer’s original proposal, the index would include measures across three domains:

  • health outcome measures (such as mortality)
  • modifiable risk factors (such as smoking)
  • social determinants of health (such as child poverty).

The intention was that the index could be used to calculate a single measure or score of health but could also be disaggregated into its three component parts.

Composite measures are already used in various parts of public policy. One example is Ofsted inspections, which use a mix of data to produce summary ratings of school performance – placing schools in four categories, from ‘outstanding’ to ‘inadequate’. Another is NHS England’s overall patient experience score, which takes into account results from a variety of individual patient survey questions (for example, how long people wait for care, and whether doctors and nurses listened to what they had to say) and combines the results into a 0-100 score. The resulting aggregate measure of patient experience can be tracked and compared over time.

In a similar way, we would expect a composite health index to be designed to provide a simple summary of progress over time – though we don’t know exactly what this would look like.

Composites can be attractive to policymakers. They promise a simple, easy-to-understand summary of performance in whatever is being measured, rather than a long list of indicators that may feel hard to interpret. They can be easy to communicate (‘things are getting better’). And they offer a potential political tool to push issues up the policy agenda. For example, both the social mobility index and the World Bank’s Human Capital index have helped draw attention to their respective concerns.

But composites come with big problems – particularly for something as complex as people’s health.

Conceptual problems

The first set of problems is conceptual. Health is multi-faceted, shaped by the interactions of social, economic, environmental and other factors. The green paper suggests using the health index alongside GDP, but there are significant differences between the two measures. GDP captures the output created across all areas, sectors and actors (private, public and individuals) in the economy, with each of those elements captured as a cash measure. But converting the various factors and conditions that affect health into a single measure is far harder. 

The concept of health stock could be measured either in terms of people’s current subjective physical and mental state (for example, using self-rated health or subjective wellbeing measures) or in terms of medical conditions and events (such as mortality or diagnosis of disease). But equally important are the likely future trajectories of people’s health, measured through health risk factors, such as whether people smoke, have poor quality housing, or have high levels of work-related stress.

Understanding these multiple dimensions of health – both current and future – would need many indicators, each providing different and valuable information with potential relevance for policy. A summary health index combining some set of these indicators may provide superficial clarity, but would mask the more complex reality that sits underneath. It could also obscure good or poor outcomes, or improving or declining outcomes, on indicators within the summary score.

Technical problems

The second set of problems is technical. The choice of indicators that make up the composite will partly be driven by what indicators are available – availability bias – and may not accurately reflect the concept being measured. Even if a single set of indicators for health is agreed, how will the constituent parts be combined and weighted to construct a single score or measure of health?

Previous experience shows that this is no simple task – even for much simpler concepts than health. For example, the choice of weights for different indicators (in other words, the relative importance given to indicators when combined to form a single index) will ultimately be down to value judgements. These judgements can’t be dodged: simply giving each item equal weighting (the same as giving them ‘no’ weighting) implies that each measure is equally important for our health. 

To confuse things further, if several of the indicators are highly positively correlated with one another, then equal weighting could produce skewed results. And all of these assumptions would likely need to be reviewed regularly to maintain relevance. But this isn’t simple either, as any changes to indicators and weightings would make the summary score less comparable over time.

Behavioural problems

A third set of problems is behavioural – in other words, how decision makers respond to the index. Policy priorities may be distorted by the choice of indicators included in the index, or the way they are weighted, at the expense of those that are not. And aggregation of indicators may disguise poor performance on some measures, potentially reducing incentives to address them. 

A major risk of a composite health index is its potential to mask inequalities. While it could be designed in a way to track health inequalities (for example, it could be presented for different levels of deprivation) there would be a danger that improvements in inequalities in one indicator (say, smoking prevalence) could mask deteriorations in another area (such as mortality). 

A single national figure could also mask trends in health inequalities between regions. This could be addressed by providing data at appropriate sub-national levels. However, getting the right footprint for regional calculation of the index would be a complex task, guided by administrative boundaries and the availability of data at local level.

One country that has adopted a composite score as a top-level national indicator for evaluating policy is Bhutan, with its Gross National Happiness Index. An important feature of Bhutan’s index that it can be decomposed by demographic characteristics and by geography, as well as by its nine domains: psychological wellbeing, health, education, time use, cultural diversity and resilience, good governance, community vitality, ecological diversity and resilience, and living standards.

Ultimately, the design of any measurement framework must be driven by its purpose. While measurement is an essential tool for guiding policy, it risks unintended consequences. If the aim of the health index is to help rebalance government priorities and investment towards policies that promote health, alternatives to a composite index might be more effective in achieving it. Government departments might benefit more from tracking performance against a range of health indicators to which their policy decisions can best contribute, without combining these indicators to produce a single – potentially problematic – index.

For example, the Department for Transport might be held accountable for key health measures related to air pollution and active travel, while the impact of other departments could be judged against measures related to the food system, such as the sugar and salt content of food. Several departments may be required to make a contribution to improvements in the same indicators – and, when taken together, the indicators would need to form a coherent framework for thinking about health and the multiple factors that shape it. A similar approach has been attempted in the New Zealand Living Standards Framework (LSF) Dashboard.

Given the major issues with composites, this kind of approach – based on a framework of health indicators, rather than a composite measure that seeks to combine them into a summary health index – may be a more coherent way of using measurement to embed health considerations across government.

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3. What measures of health should we use?

A comprehensive measurement framework for health would need to draw on a range of data and sources. It would need to consider how it uses measures of ill health alongside measures of good health and measures of risk alongside measures of protective factors.

Whereas some areas of economic and social policy have headline measures (such as GDP, the employment rate or child poverty), that is not the case with health. This is partly a reflection of the complexity of the issue. 

Current measures of health

Currently, the most widely used summary measure of health is life expectancy. This provides a useful overarching measure of progress because it is based on death rates. However, there are significant lag times between changes in the population and changes in life expectancy. For this reason, it doesn’t capture the current health of the bulk of the population, nor the experiences that influence their health. Healthy life expectancy is an alternative measure but has similar drawbacks to life expectancy (which is, in any case, used in the calculation).

While self-rated health is captured in some surveys, it is rarely used as a standalone indicator and can be hard to interpret. Also, depending on methods and sample sizes, survey-derived metrics may fail to hold up robustly at a local level – something that is vital to understand trends and evaluate policy, not just to track broad population health.

The NHS has access to a great deal of administrative data, which may provide a more objective assessment of people’s health (or, rather, illness) than self-reported surveys, and can be robust at a local level. However, the NHS can only capture the point at which a person’s health needs becomes sufficiently acute that they interact with the health care system. So, this would not, in itself, capture the overall health stock of the nation.

Alternative options

Alternative options that are more appropriate to a measure attempting to sum up health stock might include indicators such as obesity, drug and alcohol abuse, and sexual health. These form a key part of the existing Public Health England suite of indicators (the Public Health Outcomes Framework). The quality of this data at a local level is variable but it would help target action on specific health problems as well as giving an indication of future health outcomes.

All the measures described above provide some indication of people’s current and likely future health, but these health outcomes are themselves a consequence of the social, economic, commercial and environmental conditions in which people live: the wider determinants of health. 

So, to be effective in highlighting decisions that could improve or diminish people’s health, some of these wider determinants would need to be taken into account. For example, New Zealand’s Living Standard’s Framework includes measures across housing, social connection, income and consumption, alongside measures of mental health, physical health, and subjective wellbeing.

Identifying the components of a health measurement framework and exploring the most appropriate way to combine them are important stages in moving towards a greater recognition of the importance of health to the country. But it is equally important to ensure that the measure is effectively used to make health-improving policy choices, and that it becomes an accepted part of decision-making processes for the long term.

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4. How could government get the most out of a national health measure?

The success of any framework that assesses the nation’s health will depend as much on how it is used as on its technical validity. A measure that is ignored by policymakers or subject to token review will not help change priorities in the way that’s needed. 

Experience suggests that the effectiveness of key metrics in government depends on how much political support they receive. For example, the movement to create, publish and make greater use of measures of wellbeing in the policymaking process was originally championed by David Cameron as Prime Minister. The Office for National Statistics now regularly publishes a dashboard of measures of wellbeing, and in recent years wellbeing has greater recognition within the Treasury’s green book (the guide to assessing policy choices). 

However, with prime ministers and their priorities changing over time, wellbeing does not yet appear to have become a key deciding factor in government decision-making processes in the way that changes to household income, or impacts on employment are taken into account.

Supportive structures

Any index is far more likely to be effective, and to survive changing political priorities, if it is accompanied by other supportive structures, such as an independent body that monitors progress by government. The independent commissioner model is an approach that has been employed in England (through the Children’s Commissioner and Social Mobility Commission) and in Wales (through the Future Generations Commissioner). 

Given the importance of long-term investments to support a healthy population, any index would have limited effectiveness without appropriate incentives. Legislation, such as the Welsh Well-being of Futures Generations Act, is one way to embed long-term thinking in government. It places a legal duty on public bodies to work together in the interests of promoting long-term sustainable development.
However, any legal duty to consider wider issues in policymaking can become a tick-box exercise. For example, health impact assessments are required for key government decisions but have not had their intended impact. 

So, if a legislative duty to consider health metrics across government is implemented, it will be important to consider how to enforce the spirit, as well as the letter, of that duty. In Wales, the implementation of the Well-being of Future Generations act is backed by additional structures. At the national level, the Future Generations Commissioner has a remit to provide high-profile independent scrutiny of the implementation of the Act. Locally, multi-agency public service boards were set up to deliver the wellbeing goals of the Act. 

Cross-party consensus

Even formal supportive structures in themselves are no guarantee of long-term implementation unless cross-party consensus is forged on an issue. For example, the Child Poverty Act 2010 created a legal commitment to end child poverty by 2020, but new legislation was later taken to abolish the legal duty entirely. By contrast, the introduction of auto-enrolment for pensions, which began under a Labour government in the 2000s, was developed by the coalition government at the start of this decade and continued to be implemented under Conservative governments since 2015. A clear and well-evidenced case for reform has helped maintain political buy-in to the policy aim of widespread private provision. Forging such a consensus for health improvement would be an important step if a health measurement framework is to be used effectively and would help leverage long-term investment to improve health.

Putting health at the heart of government

Many key decisions about health are made at local level. So, in order to ensure greatest value, a health measurement framework would also need to be integrated into local decision making processes. Indeed, the devolution of powers to combined authorities (for example, Greater Manchester and West Midlands) has made it increasingly important for long-term considerations to be taken into account at sub-national levels. 

Internationally, there will be opportunities to learn from New Zealand, where the government published its first Wellbeing Budget in 2019. This budget required ministers in all government departments to show how their funding bids would contribute to wellbeing priorities. Allocations were based on wellbeing analysis, taking into account economic, social, environmental and cultural outcomes for current and future generations.

It is too early to tell what impact this will have, and whether it will be adopted for the long term. However, the intention was to move beyond GDP as a primary measure of policy success and to put wellbeing considerations at the heart of government in a systemic way.

If done well, an index or health dashboard could help decision makers in local authorities and combined or regional authorities to make better long-term decisions about investing in the conditions that create good health. These might include, for example, guiding complex decisions about investment in short-term health and social care services versus services such as Children’s Centres, which have a long-term impact. However, to have an impact, any new measure would need to have the right supporting structures around it. 

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Conclusion

With life expectancy stalling and inequalities in health growing, there is an urgent need for decision makers across government to treat health as one of the nation’s most important assets. A national measurement framework for health, if well constructed and implemented, could play a part in this.

This is likely to need a suite of health indicators rather than just a single-figure index, although a headline measure of health could be used in addition to sit alongside other top-level indicators such as GDP and employment. The metrics included would need to be broad enough to encompass the wider determinants of health and subjective measures of wellbeing, as well as more conventional measures such as morbidity and mortality. 

However, the challenges are not solely technical. Any new measure would need to be taken seriously by government and garner strong cross-party and public support. Government must also find ways to quickly embed any new measures within its processes. This might mean:

  • mandating the use of the measures in key decision-making processes such as departmental spending allocations
  • developing a legislative framework to require decision makers to take into account the long-term health consequences of their actions
  • setting up an independent office to hold government to account. 

Without the right supporting structures around it and a consensus for its use, any new national measure of health – however well constructed – risks simply measuring rather than influencing, and becoming sidelined when the public or political mood shifts. But a credible, widely accepted national means of measuring the nation’s health that genuinely drives decision making could make a real difference to the future of health in this country.

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Further reading

Consultation response

Advancing our health: prevention in the 2020s

Health Foundation response to the Cabinet Office and Department of Health and Social Care Advancing our health: prevention in...

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A briefing that asks: what is the social and economic value of maintaining and improving people's health?

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