ISPOR – Sunshine, Sangria and Systematic Reviews

This week, the leading minds of the pharmaceutical world gather to delve into the intricacies of health economics and outcomes research—better known as HEOR (because, of course, there's an acronym). Health Economics is a phrase that, for most people outside the market access bubble, causes eyes to glaze over faster than reading the terms and conditions of their gym membership. Yet, it takes centre stage at this year’s annual European get-together in sunny Barcelona, where data, policy, and economics will mix with tapas and maybe a little Sangria. But why is it so important in the pharmaceutical industry and healthcare in general and what are the key issues?

Why does this healthcare business all cost so much?

As life expectancy increases, so does the likelihood of developing chronic conditions—and thanks to medical advancements, we’re now living longer with these illnesses than ever before. Take the average 85-year-old nearing the end of life: they are typically prescribed between 6–12 medications, depending on their health conditions, care setting, and the complexity of service delivery involved. With longer lifespans come greater medical needs—and those needs come with a hefty price tag. Managing chronic illnesses and polypharmacy (the use of multiple medications) represents a growing challenge for healthcare systems worldwide, driving costs ever higher as the population ages

Given the absence of a "magic money tree," policymakers and healthcare providers must make tough decisions about the allocation of limited resources. Key questions they face include determining how much to pay for a given treatment and, perhaps more critically, deciding who should receive that treatment. For instance, in the context of scarce healthcare resources, it may be necessary to prioritise treatment for those with the greatest need or who are most likely to benefit, especially when it comes to high-cost therapies. Balancing cost-effectiveness with fairness is essential in shaping healthcare systems that provide value while remaining financially sustainable

This really shouldn’t be that complicated—if it weren’t for the fact that doctors aim to treat anyone who could benefit from a new intervention, patients expect the freedom to choose from the latest treatments, and the pharmaceutical industry wants to price their products in a way that reflects the considerable investment made in bringing them to market. On the other hand, the public is far from thrilled about higher taxes, bigger insurance premiums and co-pays, or out-of-pocket expenses. The gap between these two positions is about as wide as a patient’s eyes when they hear the words “colonoscopy prep”—and let’s face it, that’s pretty wide

What am I paying for anyway?

We don't cure most diseases; instead, we manage them (think Cardiovascular Disease, Diabetes, Alzheimer’s and Parkinson’s for example) and so, when evaluating the value of treatments, we often look at quality of life. Tools like the Quality-Adjusted Life Year (QALY) and Disability-Adjusted Life Year (DALY) are commonly used to quantify this value. However, these measures can be controversial because they can oversimplify or fail to fully represent the experiences of different populations, such as the elderly or disabled. For instance, using a standard QALY or DALY approach might not capture the full societal or emotional value of extending life in these groups, potentially undervaluing interventions that improve quality of life in ways beyond mere survival

Balancing cost-effectiveness with equity is a persistent challenge in healthcare policy. The central question is whether resources should prioritise the most cost-effective treatments—like certain vaccines, which offer broad population benefits—or focus on interventions that benefit the most vulnerable groups, such as the very young or elderly, who may have higher health risks and needs

This tension between efficiency (cost-effectiveness) and fairness (equity) is a key issue in health economics. Policymakers must consider both the economic value of interventions and their social impact, particularly when addressing health disparities that disproportionately affect certain populations. Balancing these factors often requires nuanced decisions that may not be easily reconciled, reflecting the broader ethical and economic debates in public health

I need to provide more data?

Then there is all that extra data needed for policy makers to make decisions in the form of Real-world evidence (RWE). RWE fills data gaps left by clinical trials by leveraging data collected from everyday healthcare settings, such as electronic health records (EHRs), claims databases, and patient registries. This approach is particularly valuable for addressing questions that randomised controlled trials (RCTs) cannot fully answer due to their controlled environments, limited populations, or predefined endpoints. Then integrating all that real-world data into health economic models can be technically and logistically difficult, particularly when data comes from very diverse sources

So what's all this modelling stuff about then?

This reminds of a well-known joke where a mathematician, an accountant and an economist apply for the same job

The interviewer calls in the mathematician and asks "What does two plus two equal?" The mathematician replies "Four." The interviewer asks "Four, exactly?" The mathematician looks at the interviewer incredulously and says "Yes, four, exactly."

Then the interviewer calls in the accountant and asks the same question "What does two plus two equal?" The accountant says, "On average, four - give or take ten percent, but on average, four."

Then the interviewer calls in the economist and poses the same question "What does two plus two equal?" The economist gets up, locks the door, closes the shade, sits down next to the interviewer and says, "What do you want it to equal?”

But there is a serious point to be made here. Long-term modelling in health economics often rests on big assumptions that can introduce a lot of uncertainty, especially for newer therapies with limited data. These models are incredibly complex—so much so that they’re often too intricate for key decision-makers to fully grasp, which diminishes their usefulness. Explaining a health economic model to a pharmacist or GP is a bit like handing a soccer coach the keys to the Hadron Collider and asking them to run an experiment, it’s not exactly going to improve the game

Why is nothing ever the same?

Health economics must adapt to vastly different healthcare systems, from single-payer systems to market-driven models. More challenging still, clinical practice varies across countries meaning, for example, that the standard of care can vary and this introduces yet more complexity

These are just the common themes, there is a lot more on the agenda

I have a headache

So, that’s a lot to think about and discuss in Barcelona, isn’t it? Time for a siesta!