Two drugs, same total health, different winners.

HTA is blind to distribution.

Here is the uncomfortable fact at the centre of this lesson: standard HTA maximises the total health of the population, and is structurally blind to how that health is distributed. The whole apparatus (QALYs, the ICER, the threshold) adds health up into a single sum and asks how to make that sum as large as possible per pound. Whose health it is never enters the arithmetic.

The principle that encodes this is one you met in the last lesson: "a QALY is a QALY, whoever receives it." A year of full health gained by a wealthy 40-year-old counts exactly the same as one gained by an impoverished, chronically ill 70-year-old. This sounds like admirable neutrality: a refusal to play favourites, everyone's health valued equally. And in one sense it is. But notice what it actually commits you to: it says the distribution of health is morally irrelevant, that only the total matters. If shifting resources from a sicker group to a healthier one produces even slightly more total QALYs, the maximising rule says do it, because it cannot see, and does not care, that you've just moved health from those who had less to those who had more.

Equity is the move that puts the question "to whom?" back into HTA. It insists that where health lands (who gains, who loses, whether gaps between groups widen or close) is part of what a decision is worth, not a detail to be summed away. And the moment you take that seriously, you collide with something the maximising view had kept hidden: efficiency isn't distributionally neutral. It has a direction.

Efficiency has a direction: it deepens inequality.

You might hope that maximising total health, while blind to distribution, is at least fair by accident: that it spreads health around roughly evenly because it doesn't discriminate. The opposite is true. Pure maximisation systematically directs resources toward the already-healthy and away from the worse-off, and it does so precisely because it's efficient.

Here's the mechanism. Worse-off groups are, as a rule, more expensive to treat for the same health gain. That's not a coincidence: it's bound up with why they're worse off in the first place: poorer access to care, more comorbidities, lower adherence, harder living conditions, later presentation. All of these mean a pound spent on a disadvantaged group typically buys fewer QALYs than a pound spent on an advantaged one, which is exactly the logic of opportunity cost, just now cutting along group lines. So a rule that chases the most QALYs per pound will, again and again, favour the cheaper-to-treat, which means the better-off. Maximisation doesn't just ignore existing health inequalities; it actively widens them, funnelling resources toward those who were already ahead, one efficient decision at a time. No one intends this. It falls out of the arithmetic.

You've actually glimpsed this direction once already. Back in Module 5, on analytic perspective, a productivity-inclusive costing quietly favoured the young and the employed over the retired, unemployed, and carers: "wider" turned out not to mean "fairer." That was this very same mechanism, seen from the cost side: efficiency, left to itself, leans toward the already-advantaged.

(A quick but important distinction: a health inequality is any difference between groups; a health inequity is a difference that is unfair or avoidable. Not every inequality is unjust, but the ones efficiency deepens, along lines of income and disadvantage, are exactly the kind most people judge inequitable.) So the "neutral" summation of QALYs turns out to have a built-in political direction: left unchecked, it makes the health gap worse. Which sets up the genuine dilemma.

The real trade-off: efficiency vs equity.

It would be comforting to think this is a misunderstanding: that with a cleverer method you could have full efficiency and full equity at once. You can't. Efficiency and equity are a real trade-off, rooted in the fact from the last screen: the worse-off cost more to help.

Follow it through. Because a pound spent narrowing the health gap (treating the expensive, worse-off group) buys fewer QALYs than a pound spent efficiently (treating the cheap, better-off group), every step toward equity costs you total health, and every step toward maximal total health costs you equity. They pull in opposite directions. You can have a larger, more unequally distributed sum of health, or a smaller, more equally distributed one, but not a sum that is simultaneously maximal and equally shared. There is no free lunch: buying fairness has a price, denominated in total QALYs, and buying maximal QALYs has a price, denominated in fairness.

For decades, HTA largely pretended this trade-off didn't exist: hiding behind "we just compute QALYs, it's objective." But refusing to weigh equity is not neutrality; it is choosing the efficiency end of the trade-off and declining to admit it. Every health system sits somewhere on this efficiency–equity line, whether or not it says so. The only real question is whether it chooses its position openly and defensibly, or by default and in denial. Equity analysis doesn't create the trade-off. It drags it into the light and forces an honest choice. Let's make that choice visible.

Efficiency against equity, live.

You have a fixed budget of £3 million and two groups. Group A is better-off: healthier to begin with, and cheap to treat (£10,000 per QALY). Group B is worse-off: sicker, and expensive to treat (£30,000 per QALY). Decide how to split the budget, and watch two things you'd love to maximise at once refuse to cooperate. (An illustrative model to expose the trade-off, not real figures.)

There's the trade-off you can't escape. At an equity weight of 1.0 (the "objective" cost-per-QALY setting) the maths sends every pound to the already-healthy group, maximising total health and the health gap together. Turn the equity weight up, and the optimal decision walks toward the worse-off: the gap closes, but the total QALYs drop, pound for pound. Nowhere on either slider is there a setting that maximises health and equalises it, because the worse-off cost more, and that fact makes efficiency and equity genuine rivals. Standard HTA doesn't avoid this choice; it just makes it silently, by fixing the equity weight at 1.0 and never mentioning the dial exists.

Now you.

Four theories, four different decisions.

Those weren't philosophy-seminar abstractions. Each theory, applied to the same evidence, can yield a different funding decision, which is exactly why "just include equity" is an empty instruction until you say which equity.

Take one drug: modest total QALYs, high cost, but it reaches a severely ill, disadvantaged group with no alternative. Watch it change verdict as the theory changes. Utilitarianism (standard HTA) rejects it: the total health gain is too small per pound, and distribution is irrelevant. Prioritarianism may fund it: those QALYs, landing on the worst-off, are weighted up enough to clear the bar. Egalitarianism likely funds it: it narrows a health gap, which is the goal, even at a lower total. Sufficientarianism / rule of rescue funds it: these patients are below any decent floor with nothing else, and that gives them priority almost regardless of the sum. One drug, one dataset, four defensible answers, because the disagreement isn't about the facts, it's about what fairness means.

This is why equity can't be bolted on as a technical afterthought. It's a prior question (what distribution are we even trying to achieve?) and standard HTA answers it, whether or not it admits to: it silently picks utilitarianism, the one theory that lets you ignore distribution entirely. Choosing to "stick with the objective ICER" isn't refusing to take a position on fairness. It is a position on fairness, the maximising one, just an unstated default. There is no view from nowhere here. There's only which theory you're using, and whether you'll say so.

Measuring it: DCEA and equity weights (and their limits).

So how does HTA actually operationalise equity, once it stops pretending to be neutral? Two main tools: genuinely useful, and genuinely limited.

• Distributional cost-effectiveness analysis (DCEA). Instead of collapsing everything into one population-wide sum of QALYs, DCEA models how health is distributed across groups (by income, region, ethnicity, whatever axis matters) and asks whether a technology narrows or widens the gaps. It turns "who gains and who loses" from an afterthought into an explicit output: this drug adds 200 QALYs and reduces the health gap between richest and poorest, or adds 200 QALYs while widening it. It makes the distributional consequence visible and comparable, rather than summed away.
• Equity weights. Going further, you can attach explicit numerical weights that value a QALY gained by the worse-off more than one gained by the better-off, formalising prioritarianism. A QALY for the most disadvantaged might count as 1.5, or 2. The decision then openly buys some equity at the cost of some efficiency, with the exchange rate written down instead of hidden, the same visible weights move you saw in the MCDA lesson.

But, and this is the expert's caution, neither tool resolves the ethics; they encode a chosen answer and then compute with it. Where does the weight of 1.5 come from, rather than 2.0? Someone chose it. And more subtly: which axis defines "worse-off"? Income? Region? Ethnicity? Sex? Age? Health inequalities run along all of them at once, and picking the axis is itself a normative act: you're declaring that inequality along this dimension counts and others don't, or count less. DCEA doesn't tell you what's fair; it tells you how unfair a particular distribution is, according to a definition of fairness you fed in. That's a huge advance in transparency: the values are now visible and arguable, exactly as in the MCDA lesson. But mistaking "I measured the inequality" for "I settled what's just" is the equity version of confusing transparency with objectivity. The number is only ever as principled as the theory and the axis you chose before you started counting.

What's the strongest response?

Why this matters for HTA

Equity is where an HTA practitioner most needs to see the values hiding inside the "technical" work, because the technical default is anything but neutral:

• Name your theory of fairness: you're always using one. The instant you report a plain ICER, you've adopted utilitarianism: total health only, distribution irrelevant. That may be defensible, but present it as a choice, and be ready to say why distribution is being ignored: especially when a decision reaches the worst-off, where the maximising default quietly works against them. "We just used cost-per-QALY" is not a neutral description; it's an ethical stance in disguise.
• Surface the distribution, don't sum it away. Whenever it's decision-relevant, show who gains and loses, not just the total: ideally through a distributional analysis that says whether the technology narrows or widens health gaps. A drug with a mediocre ICER that markedly reduces inequality, or a strong ICER that worsens it, is telling you something the single number physically cannot.
• Use equity weights and DCEA for transparency, never for false resolution. These tools make the fairness judgement explicit and arguable, a real gain, but they don't find the fair answer; they compute the consequences of a fairness definition and an inequality axis you chose. Be honest that the weight of 1.5, and the decision to split by income rather than region or ethnicity, are themselves value judgements. The method organises the argument about fairness; it never ends it.

There is no distribution-free way to distribute health. Every HTA decision sends health toward some people and away from others, and "maximise the total" is not an escape from that choice but one particular, aggressive answer to it. The practitioner's job is not to find the neutral method, there isn't one, but to make the unavoidable question, "whose health, and is that fair?", something a society can see and argue about, rather than something buried inside a ratio.

Equity, in one breath.

• Standard HTA maximises the total QALYs and is blind to distribution: it never asks whose health it's buying. "A QALY is a QALY, whoever gets it" sounds neutral but is a strong ethical position: that distribution is morally irrelevant.
• That position has a direction: because the worse-off are usually more expensive to treat, pure maximisation systematically favours the cheaper-to-treat better-off and widens health inequalities: no one intends it; it falls out of the arithmetic.
• So efficiency and equity are a real trade-off: narrowing the health gap costs total QALYs, and maximising QALYs costs fairness. There's no setting that does both. Which position you take depends on your theory of fairness: utilitarian (the total), prioritarian (weight the worse-off), egalitarian (close the gap), or sufficientarian / rule of rescue (a floor for all), and the same drug can be funded or rejected depending on which you hold.
• HTA operationalises equity with distributional cost-effectiveness analysis (model who gains and loses, and whether gaps narrow or widen) and equity weights (value the worse-off's QALYs more). Both make the fairness judgement explicit and arguable, but neither resolves it: the weight, and the axis defining "worse-off," are value choices fed in at the start. Transparency, not objectivity, all the way down.

Ask "how much health?" and cost-per-QALY answers completely. Ask "whose health, and is that fair?" and it has nothing to say, because it decided, before you asked, that the question didn't matter. Equity is simply the refusal to let that silent decision stand unexamined.

Every technology in this module (diagnostics, devices, multi-criteria decisions, and now equity) we've assessed after it exists, with data in hand. But the cheapest moment to shape a technology, or to prepare for it, is the earliest one: before the evidence matures, before the price is set, sometimes before the thing even works. Can HTA look forward instead of back? That's early HTA and horizon scanning, the final lesson of Module 13.