Welcome to Segment 2, CEA Approach and ICERS.

In segment 1 you considered why CEA can be useful. In this segment, you’ll learn how

the cost-effectiveness idea is translated into an outcome metric the incremental cost-effectiveness

ratio. First, let’s discuss the central CEA approach. We look at the incremental cost per standardized

unit of health gain. This could be the added cost per death averted or life year gained.

For whatever intervention you’re assessing, you have to compare to another identified

course of action. That course of action may be a standard practice like visiting doctors,

or it may be a less expensive or less intensive intervention. Whatever the , comparison, it

always has to be specified. I want to emphasize that this is one of the

critical elements of doing a CEA correctly: saying what you’re comparing to what. No intervention

can be properly considered in isolation. It’s also important to note that cost effectiveness

ratio, the cost per standardized unit of health gain, is the inverse of health gain per increment

of spending. If you get more added health per increment of spending, then you’re spending

less per added unit of health. The key outcome metric for CEA is the incremental

cost effectiveness ratio: the ICER. This is the difference in cost, represented as delta

cost, divided by the difference in health outcomes. The differences are between two

possible actions, here called A and B. Thus the ICER is the cost of one option A minus

the cost of option B all divided by the difference in life years. As I said, everything’s comparative. I want to talk more about the need for comparison,

by showing an example of what happens without comparison. Let’s say I want to examine the

cost-effectiveness of Drug A, in this table. A person takes drug A, and it costs $12,000,

and that person lives 40 years. Yet it’s clearly misleading to assign all of this health, these

40 life years, to drug A, because without drug A the person would still live 38 life

years. Thus, to understand the value of Drug A, we should not divide $12,000 by 40 and

say that $300 is cost of Drug A per added year of life.

Instead, what we care about is the change in health due to the intervention.

So, we need a comparison — an option B, so that we can think about the incremental cost

and life years. You can’t calculate an ICER when you have only one option. It has to be

comparative. The ICER Numerator is the net costs and that’s

different from the costs of implementation. Net cost is program costs adjusted for the

resulting changes in medical costs. Medical costs can fall if you avert disease, with

a prevention strategy such as vaccination or HIV prevention through needle exchange.

If you avert disease, you save the cost of treating that disease.

On the other hand, sometimes an intervention can increase healthcare costs. If you have

a screening program you find people with disease. Obviously, you may start to treat them. Or

you have a plan to deliver antiviral therapy earlier in disease. Added or earlier care

will increase the health care costs. So when you put all of these factors together

and compare the program costs to the health care costs you arrive at the net cost of A

— the cost of the program adjusted for induced costs or savings, minus the same type of net

cost for B. That’s the numerator. The denominator is the difference between

options A and B in terms of one of several possible measures of health. In one cost-effectiveness

report, you can examine multiple outcomes. Let’s start with natural health events. Examples

are new infections, deaths averted, life years, or disease episodes. It’s fine and in fact

it’s important to present the effectiveness of the program in terms of outcomes that people

can understand particularly when we’re presenting to clinicians and other non-economists. Everyone

can understand if you say it costs $1000 to avert a new infection or $500 to prevent a

death or $50 per added life year. This is the kind of statement that makes sense to

all of our health colleagues. However, there are limits to what kind of

comparisons we can make when we use natural health events. It’s hard to compare a measles

episode prevented to an added life year. This is why we use metrics like DALYs and

QALYs. DALYs, or disability adjusted life years, are a measure of disease burden. By

definition they are undesirable — we want to avert DALYS. QALYs are a measure of health

status and therefore they’re desirable. We want to gain them.

Why do we go to the extra trouble of DALYs and QALYs? It’s because we want to capture

all of the effects of disease. If we look just at life years, we capture only the reduced

mortality benefits of intervening. If we talk about quality of life we might capture the

effects of reducing morbidity. If we want to put it all together and compare across

different diseases that’s when DALYs and QALYs become helpful, indeed essential. When we put the numerator and denominator

together, we get the ICER: the difference in net costs divided by DALYs averted. Note that the order of the subtraction in

the numerator and denominator is different, because the numerator is added costs but the

numerator is averted DALYs. In segment # 3, we take a closer look at DALYs

and QALYs, over-arching, uber metrics in health.