The Data

A health department must choose between three programs, each targeting a different condition. The outcomes differ in type and magnitude. Which provides the most health benefit? (Data are simulated for illustration.)

Program A: Heart Disease Prevention

Deaths prevented 12 per year
Average age at death 68 years
Life expectancy 78 years
Annual cost $1.2M

Program B: Diabetes Management

People treated 400 per year
Quality improvement Mild to moderate
Years of benefit 10 years each
Annual cost $1.2M

Program C: Mental Health Services

People treated 200 per year
Quality improvement Moderate to major
Years of benefit 5 years each
Annual cost $1.2M

Next: Why comparing raw outcomes can lead to poor decisions.

The Problem

Raw outcome counts obscure important differences. A death prevented is not equivalent to a quality-of-life improvement. The magnitude and duration of benefits matter.

What Gets Lost in Simple Counts

What We Measure What It Misses
"12 deaths prevented" At what age? How many years of life gained?
"400 people treated" How much did their quality of life improve?
"Moderate improvement" What does "moderate" mean in comparable terms?
"10 years of benefit" Full health for 10 years, or partial benefit?

Standard metrics include mortality reduction, incidence rates, and relative risk. Economics asks: What is the total health gain, measured in a way that allows comparison across conditions?

Next: How QALYs and DALYs solve this measurement problem.

QALYs and DALYs Explained

Quality-Adjusted Life Years (QALYs) and Disability-Adjusted Life Years (DALYs) are two approaches to the same goal: measuring health in a common unit.

What Is a QALY?

A Quality-Adjusted Life Year combines length of life with quality of life into a single measure.

  • 1 QALY = one year of life in perfect health
  • 0.5 QALYs = one year at 50% health (e.g., moderate disability)
  • 0 QALYs = death (no life-years)
QALYs = Years of Life x Quality Weight (0 to 1)

Quality weights come from population surveys that ask people to value different health states. A health state rated 0.8 means people consider it 80% as good as perfect health.

QALYs: What We Gain

QALYs measure health gained. Higher is better. Used in cost-effectiveness analysis to calculate cost per QALY gained.

0.75 QALYs/year

Example: Living 10 years at quality 0.75 = 7.5 QALYs

DALYs: What We Lose

DALYs measure health lost. Lower is better. Used in global burden of disease studies. DALY = Years of Life Lost + Years Lived with Disability.

0.40 DALYs lost

Example: A condition causing 40% disability for 5 years = 2 DALYs lost

How Programs Compare in QALYs

Program Raw Outcome Calculation QALYs Gained
A: Heart Disease 12 deaths prevented 12 x 10 years x 0.9 quality 108
B: Diabetes 400 treated 400 x 10 years x 0.15 gain 600
C: Mental Health 200 treated 200 x 5 years x 0.25 gain 250

Program B produces the most QALYs despite not preventing any deaths. The diabetes management program helps more people for a longer time, accumulating more total health gain.

Next: Try the calculator to see how changing assumptions affects which program looks best.

QALY Calculator

Adjust the assumptions for each program to see how sensitive the comparison is to these values. Quality weights and life expectancy can shift which program produces the most health benefit.

Program A: Heart Disease Prevention

12
Based on age at death vs. life expectancy
10
1.0 = perfect health, 0.5 = moderate disability
0.90
Deaths x Years x Quality 12 x 10 x 0.90
Total QALYs Gained 108

Program B: Diabetes Management

400
10
0.10 = small improvement, 0.30 = major improvement
0.15
People x Years x Quality Gain 400 x 10 x 0.15
Total QALYs Gained 600

Current Comparison

Program A
108
Program B
600

Program B produces 5.6x more QALYs than Program A at the same cost.

Next: What this means for policy decisions and resource allocation.

Key Insight

QALYs and DALYs enable comparisons that raw outcome counts cannot. They reveal trade-offs that matter for resource allocation.

Why Mortality Alone Misleads

A program that prevents 12 deaths sounds more impactful than one that "improves quality of life." But when we account for the number of people helped and the duration of benefit, quality-of-life programs often produce more total health gain.

Assumptions Drive Conclusions

The calculator showed how changing quality weights or duration shifts the comparison. These values come from studies, but they involve judgment. Sensitivity analysis reveals which assumptions matter most.

Common Units Enable Trade-offs

Health departments face real trade-offs. Spending on one program means not spending on another. QALYs provide a framework for making these comparisons explicit rather than implicit.

Limitations Remain

QALYs assume a QALY is equally valuable regardless of who receives it. Some argue a QALY for a severely ill person should count more. Equity considerations require additional analysis.

Concepts Demonstrated in This Lab

Quality-Adjusted Life Years (QALYs): A measure combining quantity and quality of life, where 1 QALY = 1 year of perfect health
Disability-Adjusted Life Years (DALYs): A measure of health lost, combining years of life lost and years lived with disability
Quality weights: Values (0 to 1) representing how desirable a health state is relative to perfect health
Comparability problem: The challenge of comparing outcomes measured in different units
Sensitivity analysis: Testing how conclusions change when assumptions vary

Key Takeaway

Preventing deaths is not automatically more valuable than improving quality of life. When we convert outcomes to a common unit, programs that help many people for longer periods often produce more total health gain than programs preventing fewer deaths. This does not mean lives saved matter less. It means that thoughtful resource allocation requires measuring all health benefits in comparable terms. This is what economists mean by "opportunity cost" in health.