Health, before medicine
July 30, 2026
For thousands of years, surviving depended on moving, eating well and staying healthy. Today we live longer, but we fall ill chronically, and our health systems still look at disease, not health. This is the story of why we invest so little in prevention and of what personalized prediction could change.
Joan Cornet Prat, President of InnoHealth Academy
From survival to chronic disease
Homo sapiens appeared around 300,000 years ago, after a long evolution. For most of that time, survival depended on physical strength, on a language that allowed cooperation between tribes, on a diet based on hunting, fishing and gathering, and on good health to face wild animals, rival groups and the harshness of the weather.
Between 10,000 and 12,000 years ago, almost simultaneously across the planet, agriculture emerged. With it came sedentary life, and, with sedentarism, new diseases, epidemics and health problems that had not existed before.
Let’s jump to today. There are no longer wild animals around the corner; more people die from excess than from hunger; most of us do jobs with little physical effort and have many alternatives to walking. We live far longer than our ancestors. And it is precisely now that chronic diseases, neurodegenerative diseases and a notable rise in cancers proliferate.
Western health systems are designed, in their own way, for symptoms and the pathologies that follow. What they almost entirely lack are personalized strategies and actions for prediction and prevention.
Prevention, a Cinderella in the closet
Prevention is often treated like a Cinderella: acknowledged in words, admired in principle, but rarely invited to the real conversations about policy and funding.
The figures confirm it. According to the OECD (Health at a Glance 2025), spending on prevention accounts for around 3% of current health expenditure, both on average across the OECD and in Spain. During the pandemic it climbed to close to 6% and, by 2023, it had already returned to 3%. Put another way: the priority knows how to rise under acute threat and fall when the threat fades.
Why does it stay so low? The explanations are structural, not a failure of the evidence:
- Misaligned time horizon. Returns take between 10 and 20 years, or more, while political and budget cycles last from 1 to 5 years. The standard discount rate (≈3.5%) lowers the apparent value of future benefits even further (Economics by Design; OHE, 2026).
- The wrong-pocket problem. Whoever invests is not the one who reaps the savings. In England, public health depends on local authorities and the NHS captures the savings from avoided admissions; any decentralized system, the Catalan one included, faces a version of this (Economics by Design, 2026).
- Accounting classification. All health spending is counted as current expenditure; there is no mechanism to recognize prevention as capital investment. It is, probably, the most fixable item on the list (Friends of Europe, 2026).
- Invisibility and attribution. The success of prevention is an absence: the disease that did not arrive. That makes it hard to attribute and, therefore, hard to defend politically (OHE, 2026).
- Rose’s paradox. The measures that bring the most population benefit give very little to each individual; that is why nobody pushes for them (Colombo et al., PLOS Medicine, 2026).
When prevention pays off: what the evidence says
The central reference is the systematic review by Masters et al. (2017), covering 52 studies in high-income countries. The mean return on investment of public health interventions was 14.3 : 1. But that is the overall figure: broken down, it ranges from 4.1 : 1 for local interventions to 27.2 : 1 for national ones and 46.5 : 1 for legislative ones (the latter based on only two studies). For a territorial initiative like the Living Lab Pirineus, the honest reference is the local 4.1 : 1, not the headline 14.3 : 1.
Globally, the WHO estimates that an additional investment of less than 1 dollar per person per year in the prevention and treatment of non-communicable diseases, across 76 low- and lower-middle-income countries, could avert nearly 7 million deaths by 2030, with returns of up to 7 dollars for every dollar invested. It is worth remembering that these are modelled projections for low-income contexts: they are not directly transferable to Catalonia (WHO, 2021).
The strongest behavioural case is the Diabetes Prevention Program: the intensive lifestyle intervention reduced the incidence of type 2 diabetes by 58% over 2.8 years, an effect that persisted 22 years later with a 25% risk reduction. In adults aged 60 or over, the reduction reached 71%. The effect grows with baseline risk, the very mechanism of the personalization argument.
«The success of prevention is an absence: the disease that never arrived. That is why it is so hard to defend, and why it has to be made visible.»
The caveat that makes the argument credible
Here comes the honest part, and, paradoxically, the strongest. Four decades of cost-effectiveness analysis show that prevention, in general, does not save money: it tends to add medical costs rather than reduce them (Russell, Health Affairs, 2009; Cohen, Neumann and Weinstein, NEJM, 2008). One must never confuse «cost-saving» with «cost-effective».
But the same literature points to the turn: careful choices about frequency, target groups and component costs greatly increase the probability that an intervention will be highly cost-effective or even cost-saving. And concentrating the intervention where the absolute risk is genuinely high is, precisely, what personalized prediction operationalizes.
Adding a polygenic risk score (PRS) to stratification costs about £78, and a single genetic analysis can generate scores for many diseases. The evidence is real but young, and it has two limitations that must be stated clearly: it is still emerging and the performance of PRS varies by genetic ancestry, an equity risk if stratified screening is scaled before this is resolved (European Journal of Human Genetics, 2025; BRIGHT project).
Key figures (with the source and the caveat)
An honest summary of the data underpinning the article. Each figure is attributed to its source and, where the source carries one, the caveat is noted.
Figure 1. Key figures in the case for prevention. Methodological note: always distinguish «cost-saving» (returns more than it costs) from «cost-effective» (good value per unit of health gained). The WHO projections and the SDG spending pathways are models, not observed results. Text version in the Annex.
A laboratory to make prevention visible
Prevention is underfunded, in part, because it cannot show its work: its success is a disease that does not happen. A Living Lab is, precisely, an instrument to make it visible, longitudinal and continuous measurement at the individual scale that tackles the problem of invisibility and attribution head-on.
In the Pirineus Project, at InnoHealth Academy, we work in this direction: to build a health Living Lab in the Alt Pirineu that places personalized prediction and prevention at the centre, incorporates the voice of the territory’s communities and shows that health innovation can be done without leaving anyone behind. Our territory, our people.
«Prevention rarely saves money on its own. What makes it pay off is knowing whom to direct it at. That is, precisely, what personalized prediction does.»
To remember: four messages
I invite you to reflect
- → In your setting, does prevention enter the funding conversations, or does it always stay «in the closet»?
- → Which of the structural barriers, time horizon, wrong pocket, accounting, invisibility, weighs most in your context?
- → Where do you see the first realistic use case for personalized prediction in the territory?
