Cost utility analysis (CUA) is a method for comparing the costs and health outcomes of different medical treatments, programs, or interventions. It works by measuring health benefits in a standardized unit that captures both how long someone lives and how well they live, then weighing those benefits against the costs. Health systems and drug regulators around the world use CUA to decide which treatments offer good value for money and which ones don’t justify their price tag.
How CUA Measures Health Outcomes
The defining feature of cost utility analysis is its outcome measure: the quality-adjusted life year, or QALY. A QALY combines two things most people care about into a single number. It accounts for how many years a treatment adds to your life and what quality of life you experience during those years.
The QALY scale works by assigning every possible health state a value between 0 and 1. Perfect health scores 1. Death scores 0. A year lived in perfect health equals exactly 1 QALY, while a year lived with significant pain or disability might score 0.5, contributing only half a QALY. Some health states considered worse than death can even receive negative values. To calculate the total QALYs for a treatment, analysts multiply the quality score by the number of years spent in that state, then add up all the periods.
So if a cancer treatment gives someone 4 extra years of life at a quality score of 0.7, that treatment produces 2.8 QALYs. A competing treatment that gives 3 extra years at a quality score of 0.9 produces 2.7 QALYs. Despite offering fewer years, the second treatment nearly matches the first because the years are healthier. This kind of comparison is exactly what CUA is designed to reveal.
Measuring Quality of Life
The quality scores that feed into QALYs come from standardized questionnaires. The most widely used is the EQ-5D, which asks patients to rate themselves across five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. Each dimension has five levels ranging from “no problems” to “extreme problems.” A patient’s responses produce a five-digit code describing their health state, which is then converted into a single utility score using population-based preference data.
These preference weights come from surveys where large groups of people are asked to value different health states. The idea is that the scores should reflect how society, not just individual patients, values living with various conditions. This is a deliberate choice: because CUA often guides public spending decisions, the values are meant to represent a collective perspective.
Comparing Costs and Benefits
Once analysts know both the costs and the QALYs for two treatments, they calculate what’s called an incremental cost-effectiveness ratio (ICER). The formula is straightforward: take the difference in costs between the new treatment and the existing one, then divide by the difference in QALYs gained.
If a new drug costs $60,000 more than standard care over a patient’s lifetime but produces 0.5 more QALYs, its ICER is $120,000 per QALY gained. That number on its own is meaningless. It only becomes useful when compared to a threshold, a line that separates “worth paying for” from “too expensive for the benefit.”
In England, the National Institute for Health and Care Excellence (NICE) uses a threshold range of £25,000 to £35,000 per QALY gained. Treatments that fall below this range are generally recommended. In the United States, the Institute for Clinical and Economic Review uses a health benefit price benchmark of $100,000 to $150,000 per QALY gained, though it also runs analyses at the $50,000 and $200,000 thresholds. These numbers reflect what each system considers a reasonable price for one additional year of healthy life.
Discounting Future Benefits
Most health interventions involve spending money now for benefits that stretch years into the future. CUA handles this through discounting, a process that reduces the value of future costs and health gains to reflect the fact that people generally prefer benefits sooner rather than later. The standard discount rate recommended by the U.S. Panel on Cost-Effectiveness in Health and Medicine is 3% per year, applied equally to both costs and health outcomes. Some analyses also run a 5% rate for comparison with older studies.
Discounting matters most for preventive treatments. A vaccine given to a child might prevent illness decades later. Without discounting, that future benefit would look just as valuable as an immediate one. With a 3% annual discount, a QALY gained 20 years from now is worth roughly half as much as one gained today. This can make prevention programs appear less cost-effective than treatments for immediate illness, which is itself a source of debate.
How CUA Differs From Other Economic Evaluations
Cost utility analysis is actually a specialized form of cost-effectiveness analysis. The broader category of cost-effectiveness analysis can measure outcomes in any health unit: cases prevented, heart attacks avoided, blood pressure points reduced. The limitation is that you can only compare treatments for the same condition, since “heart attacks prevented” can’t be weighed against “asthma attacks avoided.”
CUA solves this by using QALYs as a universal currency. A QALY gained from a diabetes drug can be directly compared with a QALY gained from a mental health program, which makes CUA especially useful for health systems that need to allocate limited budgets across many different disease areas.
A related metric used in global health is the disability-adjusted life year, or DALY. While QALYs measure health gained, DALYs measure health lost. One DALY equals one lost year of healthy life, calculated by adding years of life lost to premature death and years lived with disability. The World Health Organization relies heavily on DALYs to compare the burden of diseases across countries, while QALYs dominate in the economic evaluations used for drug pricing and coverage decisions in high-income countries.
Ethical Criticisms of the QALY Approach
CUA is powerful precisely because it reduces complex health decisions to a single number, but that simplicity creates real ethical problems. Three stand out.
First, QALYs don’t account for how sick someone is before treatment. A treatment that moves a person from severe suffering to moderate health produces the same QALY gain as one that moves a mildly affected person to near-perfect health. Surveys in multiple countries suggest the public believes sicker patients should get some degree of priority, but standard CUA doesn’t capture that preference.
Second, QALYs favor treatments for people with the greatest capacity to benefit. If two groups have the same illness but one group responds better to treatment, CUA directs resources toward the better responders. Public opinion research suggests people are uncomfortable with this when both groups would still gain meaningful benefit.
Third, and most controversially, life-years gained by people who are already chronically ill or disabled are automatically scored lower than life-years gained by otherwise healthy people. If saving your life gives you years at a quality score of 0.6 because of a pre-existing disability, those years count as less valuable than the same years for someone without a disability. Critics argue this conflicts with the principle that everyone’s life deserves equal protection. British health economist Alan Williams proposed addressing age-related concerns through a “fair innings” concept, where QALYs for people who have already enjoyed a full lifespan of good health count for less. While this idea has some appeal for extending life in very old age, applying it to quality-of-life improvements for older people raises its own fairness issues.
These criticisms haven’t displaced CUA from its central role in health economics, but they have pushed agencies to supplement QALY-based analyses with equity considerations, severity weightings, and broader value assessments that go beyond the raw numbers.