## Dying Too Soon: How Cost-Effectiveness Analysis Can Save Lives

No. 204
Saturday, June 01, 1996
by Tammy O. Tengs

## Strategies for Health Policy

When policy analysts advocate "economic efficiency," they are suggesting that a reasonable goal of U.S. health policy is to choose the portfolio of health promotion interventions that simultaneously

• Maximizes health benefits given the resources consumed,
• Minimizes the resources consumed for the health benefits achieved, and
• Makes trade-offs between health benefits and money at a rate that reflects our true values.

Thus economic efficiency is simply getting the biggest bang for the buck, spending the fewest bucks possible for a bang and not spending more on any single bang than it is worth.

How can we achieve economic efficiency? Let's consider four strategies that are routinely advocated, implicitly or explicitly, by those charged with making decisions that affect public health:

• Invest in the interventions affecting the most people.
• Invest in the most effective interventions.
• Invest in the least costly interventions.
• Invest in the most cost-effective interventions.

We can evaluate the wisdom of each of these four strategies by referring to the 10 interventions in Table II. Holding constant the total amount that we are currently investing annually in these interventions, we can explore the ramifications of hypothetically investing this sum using each strategy. Which strategy yields the most years of life saved given the resources consumed?

"Economic Efficiency means maximizing the health benefits of the dollars we spend."

Before performing this hypothetical analysis, we first need to calculate what we are currently investing in these 10 interventions and what survival benefits we are currently realizing. We can develop a rough estimate of the level of investment in each intervention by multiplying its cost by the extent of its implementation. For example, sickle cell screening would consume \$226,876 annually if all black infants received it. Since only 80 percent of infants are currently screened, it consumes roughly \$181,501 annually (calculated as \$226,876 x 0.8).

If we estimate investments in each of the 10 interventions and add them up, we find that the set consumes \$210,029,805 in annual resources and saves 7,645 life-years annually. Now, let's hold resources constant at \$210,029,805 and consider each strategy:

"Analysis can show which strategy saves the most years of life."

Strategy #1: Invest in the interventions affecting the most people. Public health professionals routinely advocate investing in the most important public health problems or the problems affecting the most people. To explore the wisdom of this strategy, we begin by ranking interventions 1 to 10 according to the number of people affected. This ranking appears in Table III. The number of children who ride school buses probably exceeds the number of black infants born in any given year, so seat belts in school buses and sickle cell screening for black infants are ranked first and second, respectively. Further, the number of black infants probably exceeds the number of women who are pregnant and smoke, and the number of these women probably exceeds the number of people who need heart transplants in any given year.

If we invest the same \$210,029,805 according to the number of people affected, we would be able to fully implement programs 7, 2 and 1. With the leftover resources, we could ensure heart transplants for 50 percent of those who need one. In total, we would save approximately 8,999 years of life.

Strategy #2: Invest in interventions that save the most lives. Now suppose we instead took the same \$210,029,805 and invested it first in those interventions yielding the greatest number of years of life saved, ignoring other considerations. To follow this strategy, we rank interventions as they appear in Table IV. We would first make sure that physicians advise pregnant women to stop smoking, saving 6,568 years of life annually. Next, we would pay for heart transplants for everyone who needs them because doing so yields 2,915 years of life. At the bottom of the list would be banning asbestos in automatic transmission components, which saves only 0.000333 years of life annually.

If we worked our way through the list from top to bottom, investing the same \$210,029,805 until it ran out, we would find that we could afford the smoking cessation program for all pregnant women, and heart transplants for 61 percent of those who needed them. Following this strategy, we would save 8,357 years of life annually.

Strategy #3: Invest in the least costly interventions. Some advocate investing in those interventions that consume the fewest resources. Table V depicts the 10 interventions ranked from low to high according the total annual cost of the program. Smoking advice for pregnant women actually saves more money that it costs, taking into account the avoided cost of treating smoking-related illnesses. Thus smoking cessation advice is ranked first. Next, banning asbestos in automatic transmission components consumes few societal resources at \$22,112. Ranked last, heart transplants would consume more than \$460 million if everyone who needed one received one.

If we invest the same \$210,029,805 in the least costly interventions first, we could fund 100 percent of every program except heart transplants, with enough left over for 26 percent of those who need transplants. The result would be 8,367 years of life saved.

Strategy #4: Invest in the most cost-effective interventions. Finally, suppose that we made investment decisions based on cost-effectiveness. Table VI ranks the interventions according to cost per life-year saved. If we followed this strategy, we would begin by making sure that physicians advised their pregnant patients to stop smoking because the cost/life-year ratio is < \$0. Next, we would make sure that all black newborns were screened for sickle cell because the cost/life-year ratio is \$236. Our last priority would be banning asbestos in automatic transmission components at a cost/life-year of more than \$66 million.

Using this strategy, we find that we could spend the same \$210,029,805 by funding the first three programs and heart transplants for 61 percent of those who need them. If we did so, we would save 9,325 years of life.

"Using cost-effectiveness information allows us to make trade-offs between survival (quantity of life) and costs (all other goods and services)."

As shown in Table VII, funding the most cost-effective interventions first saves more years of life than any other strategy. Further, if we had performed a different experiment, one in which we specified a number of life- years to be saved and sought the strategy that would minimize costs, cost-effectiveness would again have proven superior. Finally, using cost-effectiveness information strategically allows us to make trade-offs between small improvements in survival (i.e., quantity of life) and costs (i.e., all other goods and services).

These results are not a fluke, and the superiority of the cost-effectiveness strategy is not specific to the interventions chosen for this example. Basing welfare decisions on some measure of the relationship between costs and benefits will always prove superior to any other strategy, when the goal is to maximize benefits given the resources consumed.