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The jobs we do, the places we live and the money in our pockets all influence our health. Health care also contributes, but unpacking the extent of its contribution can be challenging. For instance, the soundbite that the NHS only contributes 10–20% to our health and wellbeing has gained currency and helped to emphasise the key role of the building blocks of health alongside health care. But where does this statistic come from and what does it really mean?

Studies estimate the contribution of health care to health in two main ways. One looks at variation in health between populations and the other at health trends over time. Neither method is perfect nor static – different populations, time periods and measurements produce different results. And despite taking a broad view of what shapes health, most studies measure health using narrow metrics such as life expectancy and prevalence of physical or mental illness. Healthy life expectancy, quality of life and wellbeing are often not considered.

The variation method

The variation method looks at the health of different groups of people. Data on various aspects of people’s lives are then used to identify which factors cause those differences in health. One often-quoted study from the US estimates that 20% of the variation in health outcomes can be explained by differences in health care. This includes access (eg whether someone has insurance) and quality of care (eg whether care was timely). Social and economic factors (eg education, income and family support) contribute 40%. Health behaviours (eg smoking, diet and exercise, alcohol use) contribute 30%. The physical environment (eg air, water and housing quality) contributes the remaining 10%. This model suggests that if everyone in the US had similar physical environments, social and economic support, and health-related behaviour then variation in health could be reduced by up to 80%.

The problem with this method is that it solely seeks to explain what causes the variation in health outcomes, but not what contributes to overall levels of health. And the cited study uses data from the US – other countries have different levels of economic inequality and different health care systems, which means they make different contributions to health. In a setting where everyone has good access to high-quality health services, this method would show that health care contributes almost nothing to the variation – even though health services clearly affect the level of health in the population. The approach reflects not only how much health care contributes but how much variation there is in its use in the population.

The over-time method

The over-time method looks at health trends, such as increases in life expectancy, over a long period of time. It identifies the proportion of the changes due to actions within health care, versus those outside it. For example, we might estimate how much a reduction in infectious disease mortality could be attributed to health care (eg antibiotics) versus other interventions (eg, better sanitation). One UK study used the introduction of the NHS in 1948 as a ‘natural experiment’, and found that for those with less access to health care before the transition to universal care under the NHS reduced their mortality by 14%. A US study used a similar method to show that 50% of a 7.5-year increase in American life expectancy between 1950 and 1990 was due to health care.

Does this mean health care contributes 14% of overall population health? Again, no, not quite. Looking at the UK study, the 14% refers to the difference between the patchy care available before and what the NHS offered, and the NHS is not ‘perfect’ health care. A health care system with better medications, treatments and other advances might improve health more, or faster – the starting point makes a big difference to the estimate of how much health is gained. 

So what are the right questions to ask? 

Both methods then have serious limitations – and neither would actually give us an estimate of the overall contribution of the NHS to our health. But these studies help to raise a more useful set of questions about what governments should prioritise and for whom. One approach might be to follow the so-called ‘rule of rescue’ and prioritise the extension of life, spending the most on those closest to death. Or, we could take a utilitarian approach, thinking about what would improve health for the most people. Or, we could take a redistributive or fairness-based approach, prioritising those with the highest need, regardless of benefit.

These ideas lead us to three interlinked questions.

  • Where is there the most scope to improve health? In the early 20th century, health care was responsible for enormous improvements in health and the NHS gave people security about meeting the costs of their health care. But can gains from the NHS go any further? Yes, we need to keep investing to make sure standards don’t slip, but perhaps there are opportunities elsewhere, such as providing early years support. Conversely, new health care developments, such as weight-loss drugs like Ozempic or personalised genetic medicine, might offer opportunities to make big inroads into major drivers of ill health.
  • Where will spending be most effective? Interventions also need to be cost effective, demonstrating return on investment. Some health care is expensive but tightly tied to health improvements, for example, new therapies have radically improved cancer survival. Some non-health care interventions are relatively cheap, like recommending everyone be active each day. But demonstrating their link to better health, at least in the short term, may be difficult to achieve.  
  • What is the fairest thing to do? Then there are questions about how we distribute resources. Should we take our cue from the variation studies and focus spending where there are the greatest differences between groups? The NHS is arguably relatively universal, but the building blocks of health are not evenly distributed. Might a government want to focus on the drivers of health that lead to the greatest inequalities?

Answering these questions isn’t easy and short-termist thinking and spending makes it more difficult. More evidence and data – balancing costs against likely impact on population health, and understanding the impact of the wider determinants on health outcomes – can help us identify where there is the greatest value and scope for improvement. This evidence needs to be used as part of a long-term and cross-government approach to take action that will really have an impact on people’s health. 

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