Equity

Why this matters in health economics

Every health system spends public or pooled money, and every allocation of that money makes some people better off than others. Efficiency asks how to get the most health from a budget; equity asks how that health is shared out, who pays for it, and whether the distribution is just. A system can be efficient and grossly unfair — concentrating gains on those already healthiest and richest — and most societies treat that as a failure, not a success. Because health touches survival, dignity, and opportunity, fairness in health carries moral and political weight that fairness in most other markets does not.

The stakes are concrete. In many countries, out-of-pocket charges push millions of households into poverty each year, while the poorest often receive the least care despite the greatest need. Statutory duties frequently make equity a legal obligation, not a preference: many health authorities are bound by law to reduce inequalities, and a decision that widens them can be challenged. Getting equity wrong also erodes public trust, which is the true currency of any pooled system — people contribute because they believe the system treats them fairly.

For a director, equity is not a soft add-on to the business case. It changes which options you shortlist, how you measure success, and how you defend a decision when someone asks, reasonably, why their community was last in line. This chapter gives you the concepts, the measurement tools, and the honest trade-offs so you can answer that question with evidence rather than good intentions.

Core concepts

Health equity is the absence of unfair and avoidable differences in health and in access to health care. The word "unfair" is doing heavy lifting: not every difference is inequitable. A difference in health between a 20-year-old and an 80-year-old is a fact of biology; a difference between two 40-year-olds driven by income, ethnicity, or postcode is usually judged unfair because it is avoidable and arises from social arrangements rather than free choice. Equity is therefore a normative judgement, and stating your judgement explicitly is the first act of rigour.

The workhorse distinction is between horizontal and vertical equity. Horizontal equity means treating equals equally — people with the same need should receive the same care, and people with the same ability to pay should contribute the same. Vertical equity means treating unequals appropriately unequally — people with greater need should receive more care, and people with greater ability to pay should contribute more. Most disputes reduce to two questions: what counts as "equal" (need? income? risk?), and how much unequal treatment is warranted.

Equity applies at three points in the system, and they can move in opposite directions. Equity in finance concerns who pays: a financing system is progressive if the rich contribute a larger share of their income than the poor, and regressive if the poor pay proportionally more — as they typically do when care is funded by out-of-pocket charges or flat premiums. Equity in delivery concerns who gets care: the common standard is "equal access, or equal utilization, for equal need", irrespective of income, geography, or group. Equity in outcomes concerns the resulting distribution of health itself — the health inequalities between rich and poor, regions, and social groups. A country can have a progressive financing system yet still show large outcome inequalities, because health is shaped far more by the social determinants of health — income, housing, education, work, environment — than by health care alone.

Two empirical regularities anchor the field. The inverse care law, articulated by Julian Tudor Hart, holds that the availability of good medical care tends to vary inversely with the need of the population served — those who need most often get least. The Whitehall studies of British civil servants showed a graded relationship between social position and mortality: not merely the poorest versus the rest, but a smooth social gradient in which each step down the hierarchy carries worse health. The gradient matters because it means inequality is not a problem only at the bottom.

Underlying every equity choice is a theory of distributive justice. An egalitarian view seeks to equalize health or access; a maximin (Rawlsian) view prioritizes the worst-off; a utilitarian view maximizes total health regardless of distribution; and a needs-based view allocates by capacity to benefit or by severity. These are rival, not complementary — you cannot maximize total health and prioritize the worst-off at the same time when they conflict. The capability approach associated with Amartya Sen, which reframes fairness around what people are able to be and do rather than health states alone, is developed in Chapter 3.5 — Capabilities.

It is worth naming these theories precisely, because each licenses a different equity weight and each can honestly claim the label "fair". Utilitarianism counts every unit of health gain equally and maximizes the total — which is exactly what an unweighted quality-adjusted life year (QALY) does, making standard cost-effectiveness analysis quietly utilitarian rather than value-neutral. Prioritarianism keeps the goal of more health but gives extra moral weight to gains for the worse-off, so the same QALY counts for more when it accrues to someone sicker or poorer; a formal equity weight is prioritarianism turned into a number. Strict egalitarianism values equality of health in itself and, at the limit, can even favour levelling down. The maximin or Rawlsian view concentrates on the position of the worst-off, and Norman Daniels' "just health" extends John Rawls's principle of fair equality of opportunity to health care — grounding a right to care in the life chances that illness forecloses rather than in health as such.

Two further positions matter in practice. Sufficientarianism holds that what justice requires is not equality but that everyone reach a threshold of "enough" — enough health, or enough access — after which residual inequality is less pressing; it underwrites benefit packages and minimum standards rather than gap-closing for its own sake. Alan Williams' "fair innings" argument holds that everyone is entitled to a normal span of health over a lifetime, so a young person facing early death has a stronger claim than an older person who has already had their innings — a view that can justify weighting health gains by age, and that pulls against the utilitarian instinct to fund whoever offers the most QALYs per pound. The practical lesson is that "equity" is not one thing: these are rival theories that give different, defensible answers to the same allocation, and an equity weight is only as legitimate as the theory of justice it silently encodes.

Measurement gives these ideas teeth. The dominant tools are the concentration curve and concentration index, developed for health by Adam Wagstaff and Eddy van Doorslaer. The concentration curve ranks the population from poorest to richest along the horizontal axis and plots the cumulative share of health, illness, or health-care use on the vertical axis; if the curve lies above the diagonal, the variable is concentrated among the poor, and if below, among the rich. The concentration index summarizes that curve in a single number between −1 and +1, where zero means no income-related inequality — conceptually the same family of tools as the Gini coefficient and the Lorenz curve used for income inequality. Because sicker groups legitimately use more care, analysts apply need-standardization to strip out variation that is warranted by need, leaving a horizontal inequity index that isolates the unfair part. For financing, the Kakwani index measures progressivity as the gap between how payments are distributed and how income is distributed. These methods let you say not just "that looks unfair" but "care is pro-rich by this much, after accounting for need."

Finally, equity almost always trades off against efficiency. Reaching the last isolated household, or treating the sickest for whom care is least cost-effective, usually costs more per unit of health than concentrating resources where they go furthest. Naming that trade-off, rather than pretending it away, is the mark of honest analysis — and links directly to how systems ration (see Chapter 3.3 — Rationing) and evaluate (see Chapter 2.1 — Economic Evaluation).

Best practices

1. State your definition of equity before you measure anything. Equity is a normative choice, and different definitions point to different decisions — equalizing outcomes, prioritizing the worst-off, and maximizing total health can each be called "fair". Write down which conception you are using, whose fairness you are protecting (income groups, geographies, ethnicities, ages), and along which dimension (finance, delivery, or outcomes). An unstated definition is not neutrality; it is a hidden judgement that no one can challenge.

2. Separate finance, delivery, and outcomes, and measure each. These three faces of equity move independently: a progressive tax-funded system can still deliver care unequally, and equal delivery can still leave large outcome gaps because health is mostly made outside the clinic. Diagnose which one you are trying to fix before choosing a lever, because the tools differ — financing reform for who pays, service redesign for who gets care, and cross-sector action on the social determinants of health for outcomes. Conflating them wastes effort on the wrong instrument.

3. Choose horizontal or vertical equity deliberately for each decision. Ask whether the goal is to treat like cases alike (horizontal — same need, same care) or to tilt deliberately toward greater need (vertical — more for the sicker or poorer). Screening programmes and waiting-time standards are usually horizontal-equity problems; targeted outreach, subsidies, and priority for deprived areas are vertical-equity problems. Confusing the two produces incoherent policy, such as a "universal" offer that quietly favours those who find it easiest to access.

4. Measure inequity with need-standardized methods, not raw gaps. A raw difference in utilization between rich and poor may be entirely appropriate if the poor are sicker, so use concentration indices with need-standardization to isolate the unfair component. Present the horizontal inequity index alongside the crude figure so decision-makers see how much of the gap is warranted by need and how much is not. Robust measurement moves the conversation from anecdote to evidence and makes progress trackable over time.

5. Track financial protection, not just health gain. A system can improve average health while bankrupting the households that fall ill, so monitor out-of-pocket spending, catastrophic health expenditure, and impoverishment due to health costs. Financial risk protection is a core goal of universal health coverage and a distinct equity outcome from health itself. Use the Kakwani index or equivalent to check whether the way you raise money is progressive or quietly loads costs onto the poor.

6. Make the equity–efficiency trade-off explicit and quantified. Reaching the worst-off usually costs more per unit of health, so state the trade-off in numbers rather than burying it: how much health are you willing to forgo, and for whom, to narrow a gap? Techniques such as distributional cost-effectiveness analysis and explicit equity weights let you model the trade-off instead of resolving it by assertion. Decision-makers can then own the value judgement openly, which is where it belongs.

7. Use equity weights or distributional analysis rather than a footnote. Standard cost-effectiveness analysis implicitly weights everyone's health gain equally, which is itself a distributive choice — and often not the one your organization would endorse. Distributional cost-effectiveness analysis shows how options change both total health and its distribution, so a slightly less efficient option that reduces inequality can be chosen with eyes open. If you cannot quantify weights, at least present the distributional consequences beside the efficiency result rather than relegating equity to a paragraph at the end.

8. Design for the inverse care law, because default systems reproduce it. Left alone, services tend to reach the articulate, mobile, and well-connected first, so equal provision on paper becomes unequal use in practice. Build in proportionate universalism — a universal offer with intensity scaled to need — through targeted outreach, translated materials, transport support, and placing capacity where deprivation is greatest. Assume that a service which does nothing special for the hardest-to-reach will widen the gap it was meant to close.

9. Disaggregate every headline metric. An average that improves can hide a gap that widens, so break down access, quality, and outcomes by income, geography, ethnicity, disability, and other relevant axes before declaring success. Insist that dashboards show distributions and gradients, not just means, and set inequality-reduction targets alongside performance targets. What is not disaggregated cannot be governed, and equity dies in the aggregate.

10. Act on the social determinants, and partner beyond health care. Because most of the health gradient is produced by income, housing, education, and work, the highest-leverage equity actions often lie outside the health service's direct control. Use your economic evidence to make the case for cross-sector investment and to value the health returns of action in other sectors. Treat health care as necessary but insufficient for outcome equity, and build the partnerships and pooled accountability that make upstream action possible.

11. Guard against equity harms from new technology and reform. Market-style reforms, digital channels, and algorithmic tools can each widen gaps — competition can favour easier-to-treat patients, digital-first access can exclude those without connectivity or confidence, and biased models can under-serve groups under-represented in data (see Chapter 5.3 — AI Health Economics). Run an equity impact assessment before rollout, not after, and monitor uptake by group during it. Innovation is not equity-neutral; assume it will concentrate benefit unless you actively distribute it.

12. Build procedural fairness around the substantive choice. Because reasonable people disagree about what is fair, the legitimacy of an equity decision rests partly on how it was made — transparent criteria, relevant reasons, opportunities to appeal, and openness to revision. This procedural discipline, developed at length in Chapter 3.3 — Rationing, matters most precisely when the substantive answer is contested. A defensible process converts an unavoidable value judgement into a decision the public can accept even when they dislike it.

Questions to discuss with your team

1. Which conception of equity are we actually optimizing for, and would we defend it in public? Teams often assume they share a definition of fairness until a hard case reveals they do not — one person wants to maximize total health, another to protect the worst-off, a third to equalize access regardless of benefit. Surface this by testing your real decisions against each conception: would prioritizing the sickest, even when their care is least cost-effective, be a feature or a bug in your system? Name whose fairness you are protecting (which income groups, regions, or communities) and along which axis — finance, delivery, or outcomes. An honest answer commits to a stance, accepts what that stance sacrifices, and can be stated aloud to the people it affects without embarrassment. Beware answers that claim to serve every conception at once; that usually means none has been chosen.

2. How large an efficiency sacrifice are we willing to make to narrow a specific gap, and who decides? Equity and efficiency genuinely conflict at the margin, so refusing to name a rate of exchange between them is a decision by default — usually in favour of whatever the standard analysis already rewards. Work a concrete case: if reaching a remote or deprived population costs, say, several times more per unit of health than serving an easier group, is that worth it, and up to what multiple? Distinguish the technical question (what does each option cost and deliver, and to whom) from the value question (how much are we willing to pay to be fairer), and be clear which one your team is actually arguing about. An honest answer sets an explicit, reviewable willingness to trade, locates the authority to set it with legitimate decision-makers rather than analysts, and revisits it as evidence changes.

3. Where might our current services be quietly reproducing the inverse care law, and how would we know? The most common equity failure is not a discriminatory policy but a neutral one that lets advantaged groups benefit first, widening gaps while every headline average improves. Interrogate your own data: do uptake, waiting times, and outcomes differ by deprivation, ethnicity, disability, or geography once need is accounted for? Ask where a "universal" offer depends on resources — time, transport, digital access, health literacy — that are themselves unequally distributed. An honest answer names at least one place the team suspects it is failing the hardest-to-reach, admits where the data are too aggregated to tell, and commits to disaggregated monitoring rather than waiting for a complaint to reveal the gap.

4. Which face of equity — finance, delivery, or outcomes — are we actually trying to fix, and is our lever matched to it? Teams routinely reach for a service-redesign lever when the problem is really who pays, or promise to close outcome gaps with clinical care when most of the gradient is made upstream. Pin down which of the three faces the decision touches: is the harm that the poor contribute a larger share of income (finance), that equal need does not get equal care (delivery), or that health itself is unequally distributed (outcomes)? Each has its own instrument — progressive financing and risk protection for finance, service redesign and outreach for delivery, cross-sector action for outcomes — and using the wrong one wastes effort while the gap persists. Be honest about whether the lever you already favour is chosen because it fits the diagnosed face or merely because it is the one you control. An honest answer states the face explicitly, matches the instrument to it, and notes where the real driver lies outside your remit and needs a partner.

5. Are we tracking financial risk protection as a distinct equity outcome, or only counting health gained? A programme can raise average health while the households that fall ill are pushed into hardship by out-of-pocket charges, and a health-gain metric will never reveal it. Ask whether you monitor catastrophic and impoverishing health expenditure alongside clinical outcomes, and whether the way you raise or recover money is progressive or quietly loads cost onto the poorest. Distinguish the people who benefit from a service from the people who bear its cost — they are not always the same, and equity lives in the gap between them. An honest answer treats protection from financial harm as a goal in its own right, names the metric that would show it, and checks the progressivity of the funding mechanism rather than assuming free-at-point-of-use settles the question.

6. Before we roll out a new digital channel, model, or reform, have we assessed who it will leave behind? Innovation is not equity-neutral: digital-first access can exclude those without connectivity or confidence, competition can reward providers who avoid harder patients, and models trained on unrepresentative data can under-serve the groups least present in it. Ask whether an equity impact assessment runs before launch rather than after, and whether uptake and outcomes will be monitored by group during rollout, not just in aggregate at the end. Interrogate the hidden assumptions the innovation makes about its users — their devices, language, literacy, and trust — and who fails those assumptions. An honest answer assumes the change will concentrate benefit on the advantaged unless deliberately distributed, builds the disaggregated monitoring in from day one, and is willing to slow or redesign a rollout that widens a gap (see Chapter 5.3 — AI Health Economics).

In practice: a health economics example

The Ministry of Health in Terenga, a fictional lower-middle-income country in Southeast Asia, is expanding its national health scheme and must decide how to spend a limited new allocation for chronic disease. Two options compete. Option A places diabetes screening and management clinics in the well-connected provincial capitals, where uptake will be high and cost per case detected low. Option B funds community health workers to deliver the same package in rural and informal-settlement districts, where prevalence is higher but roads, staffing, and clinic attendance are poor, so cost per case detected is roughly three times higher.

A conventional cost-effectiveness analysis favours Option A: it detects and manages more cases per unit of spend, and its incremental cost-effectiveness ratio sits comfortably below the country's threshold. The team could stop there, but the Ministry has a statutory commitment to reduce health inequalities, so the analysts run a distributional analysis instead. Ranking districts by a poverty index, they estimate the concentration index of current diabetes care and find it is strongly pro-rich: after need-standardization, the wealthiest districts already receive far more care relative to need than the poorest. Option A, they show, would push the concentration index further toward the rich, because it adds capacity where uptake is easiest — a textbook illustration of the inverse care law. Option B moves the index toward equality.

To make the trade-off explicit rather than rhetorical, the team uses distributional cost-effectiveness analysis. They present two numbers side by side: total health gained (higher under A) and the change in the health gap between richest and poorest districts (improved only under B). They then ask decision-makers to state an equity weight — how much additional health forgone is acceptable to move a unit of gain from a rich district to a poor one. The Ministry, mindful of both its legal duty and the political cost of a scheme that visibly favours the capital, sets a weight that makes the distribution-improving option competitive despite its lower raw efficiency.

The financing side reinforces the choice. Because the rural package is delivered free at point of use and funded from progressive general taxation, it strengthens financial risk protection for households most exposed to catastrophic out-of-pocket costs — a second equity gain that the cost-per-case metric never captured. The Ministry chooses a blended rollout weighted toward Option B, sets a disaggregated target to narrow the concentration index within five years, and commits to publishing the gradient annually. The decision is less efficient in narrow terms, and the analysts say so plainly; what makes it defensible is that the sacrifice was measured, the value judgement was owned by accountable officials, and the process was transparent.

Four sector lenses

Startup

A digital health start-up piloting, say, a remote diabetes-monitoring app faces equity as both a risk and a differentiator. Its natural early adopters are younger, wealthier, and more digitally confident, so uncorrected it will widen the very gap payers care about — and payers increasingly ask for equity evidence before they buy. A disciplined founder segments uptake by deprivation from the first pilot, designs low-bandwidth and assisted-access pathways, and treats reaching under-served groups as a product requirement rather than a later feature. With small samples, formal concentration indices are premature, but disaggregated uptake and outcome tracking are not, and building them in early is cheap.

Small business

An established general-practice partnership, single clinic, or care home serves a defined local population it already knows, and its equity task is to notice who on its list is quietly falling through the gaps. Without an enterprise's analytics, it still holds the raw material — its own register — and can flag which patients miss appointments, decline screening, or never present, then break simple uptake figures down by deprivation, age, or language. Its levers are practical and close to hand: flexible appointment times, home visits, translated reminders, and a warm handoff to social support, all of which lift use among those the default offer reaches last. The steady-state constraint is capacity, not runway, so the honest question is where to spend scarce clinical time for the most equity gain rather than how to grow. Because it is embedded and durable in one community, its credibility rests on treating like need alike across a familiar caseload, not on chasing the easiest patients to keep throughput up.

Enterprise

A large provider or insurer has the scale to measure inequity rigorously and the market incentives that can undermine it. Under competition or activity-based payment, an enterprise faces quiet pressure to favour easier, more profitable patients — cream-skimming that erodes horizontal equity even without any explicit policy. Mature organizations counter this with risk-adjusted funding, equity-weighted quality metrics in the board pack, and need-standardized analyses across their whole population, not just those who present. The enterprise's advantage is data richness; its duty is to disaggregate it and to hold service lines accountable for gradients, not just averages.

Government

A ministry or national payer owns equity as a statutory and political obligation, and it commands the widest set of levers — progressive financing, benefit-package design, resource allocation formulae that weight for deprivation, and cross-sector action on the social determinants of health. Its distinctive tools are population-wide: it can shift the concentration index of an entire system, mandate financial risk protection, and legislate duties to reduce inequalities. Its distinctive risk is that averages look good while gradients persist, so government's core discipline is disaggregated public reporting and formulae that direct money toward need. Legitimacy comes from transparent, procedurally fair choices about trade-offs the public can see and contest.

Common failure modes

• Undefined equity. Invoking "fairness" without stating a conception, so the decision smuggles in a value judgement no one voted for. Fix: write the definition down before measuring, and name whose fairness and which dimension.
• Averages that hide gradients. Celebrating an improved mean while the gap between richest and poorest widens beneath it. Fix: disaggregate every headline metric and set inequality-reduction targets alongside performance ones.
• Raw gaps mistaken for inequity. Treating any difference in utilization as unfair, ignoring that sicker groups should use more care. Fix: need-standardize, and report the horizontal inequity index, not the crude difference.
• Equity as an afterthought. Running a standard cost-effectiveness analysis and adding an equity "paragraph" at the end, where it changes nothing. Fix: use distributional cost-effectiveness analysis or explicit weights so distribution enters the decision.
• Denying the trade-off. Claiming an option is both most efficient and most equitable to avoid an uncomfortable choice. Fix: quantify the sacrifice and let accountable decision-makers own it.
• Health-care tunnel vision. Trying to close outcome gaps with clinical services alone when most of the gradient is made upstream. Fix: act on the social determinants and partner across sectors.
• Equity-blind innovation. Rolling out digital or market reforms that concentrate benefit on the advantaged. Fix: run an equity impact assessment before launch and monitor uptake by group during it.
• One theory of justice smuggled in as neutrality. Presenting an unweighted cost-effectiveness result, or any single distributive rule, as the objective or the "fair" answer, when it is really one contested theory — usually utilitarianism — among several. Fix: name the theory of justice your method encodes, and show what a prioritarian, egalitarian, sufficientarian, or fair-innings view would decide instead.

Maturity model

Checklist

• We have written down which conception of equity this decision optimizes for, and whose fairness it protects.
• We have distinguished equity in finance, in delivery, and in outcomes, and chosen the right lever for each.
• We have decided, per decision, whether the goal is horizontal or vertical equity.
• Our inequity measures are need-standardized, not raw gaps.
• We track financial risk protection — out-of-pocket, catastrophic, and impoverishing costs — as a distinct equity outcome.
• The equity–efficiency trade-off is quantified and owned by accountable decision-makers, not buried.
• Every headline metric is disaggregated by income, geography, and other relevant axes.
• We have run an equity impact assessment before any major reform, digital rollout, or algorithmic tool.
• We have identified upstream, cross-sector actions on the social determinants of health where health care alone is insufficient.
• The decision process is transparent, gives relevant reasons, and allows challenge and revision.

Key sources

• van Doorslaer, Wagstaff & Rutten (eds.), Equity in the Finance and Delivery of Health Care: An International Perspective — the foundational comparative treatment of concentration-index methods.
• Wagstaff & van Doorslaer, methodological papers on measuring and explaining income-related health inequality (concentration index, need-standardisation, decomposition).
• World Health Organization — health financing and universal health coverage publications on financial risk protection and progressive financing.
• World Bank / WHO — Tracking Universal Health Coverage global monitoring reports on service coverage and catastrophic health spending.
• OECD — Health at a Glance, for cross-country indicators of access and outcomes disaggregated by socioeconomic status.
• Marmot et al. — reviews of health inequalities and the social determinants of health (the social gradient and proportionate universalism).
• Anand, Peter & Sen (eds.), Public Health, Ethics, and Equity — on distributive justice and equity in health.
• Daniels, Just Health: Meeting Health Needs Fairly — the Rawlsian case for grounding a right to health care in fair equality of opportunity.
• Williams, "Intergenerational Equity: An Exploration of the 'Fair Innings' Argument" — the seminal statement of the fair-innings view and its implications for age weighting.
• Parfit, "Equality and Priority" — the reference distinction between egalitarianism, prioritarianism, and sufficiency.
• Cookson, Griffin, Norheim & Culyer (eds.), Distributional Cost-Effectiveness Analysis — methods for building equity into economic evaluation.

References

1. Health equity — Wikipedia — https://en.wikipedia.org/wiki/Health_equity
2. Equity (economics) — Wikipedia — https://en.wikipedia.org/wiki/Equity_(economics)
3. Social determinants of health — Wikipedia — https://en.wikipedia.org/wiki/Social_determinants_of_health
4. Progressive tax — Wikipedia — https://en.wikipedia.org/wiki/Progressive_tax
5. Inverse care law — Wikipedia — https://en.wikipedia.org/wiki/Inverse_care_law
6. Whitehall Study — Wikipedia — https://en.wikipedia.org/wiki/Whitehall_Study
7. Distributive justice — Wikipedia — https://en.wikipedia.org/wiki/Distributive_justice
8. Egalitarianism — Wikipedia — https://en.wikipedia.org/wiki/Egalitarianism
9. Amartya Sen — Wikipedia — https://en.wikipedia.org/wiki/Amartya_Sen
10. Universal health coverage — Wikipedia — https://en.wikipedia.org/wiki/Universal_health_coverage
11. Gini coefficient — Wikipedia — https://en.wikipedia.org/wiki/Gini_coefficient
12. Prioritarianism — Wikipedia — https://en.wikipedia.org/wiki/Prioritarianism
13. Equity in the Finance and Delivery of Health Care: An International Perspective — van Doorslaer, Wagstaff & Rutten (eds.), Oxford University Press — https://global.oup.com/
14. Distributional Cost-Effectiveness Analysis: Quantifying Health Equity Impacts and Trade-Offs — Cookson, Griffin, Norheim & Culyer (eds.), Oxford University Press — https://global.oup.com/
15. Just Health: Meeting Health Needs Fairly — Norman Daniels, Cambridge University Press — https://www.cambridge.org/
16. Intergenerational Equity: An Exploration of the "Fair Innings" Argument — Alan Williams, Health Economics — https://onlinelibrary.wiley.com/journal/10991050
17. Health at a Glance — OECD — https://www.oecd.org/health/health-at-a-glance/
18. Tracking Universal Health Coverage: Global Monitoring Report — World Health Organization & World Bank — https://www.who.int/publications