[NARRATOR]
The last module discusses another type of economic
evaluation: cost-effectiveness analysis. As discussed in
the benefit-cost analysis module, economic evaluations
are best conducted once a program, policy, or
intervention has proven effective but prior to widespread
implementation and dissemination.
In this way, economic evaluations
are typically conducted retrospectively.
However, an economic evaluation is often
conducted prospectively, alongside community or
clinical trials to ensure efficient
allocation of scarce public health resources.
As with benefit-cost analysis, a cost-effectiveness
analysis compares an intervention’s costs
to its outcomes.
Unlike a benefit-cost analysis, a cost-effectiveness
analysis expresses outcomes in natural health
units, such as the number of cardiovascular
disease cases prevented or the number of lives
saved, instead of converting outcomes to dollars.
Because of this major difference, cost-effectiveness
analysis must be conducted with interventions
or programs that impact the same health outcome.
For example, you could compare two
programs designed to prevent overweight or obesity,
where one program focuses on physical
activity and the other focuses on nutrition.
The summary measure in cost-effectiveness
analysis is the ratio of net programmatic
costs divided by net program effects.
Programmatic costs are program costs
minus the cost of illness averted by the program.
Cost-effectiveness ratios can be an average.
One intervention at a time is assessed in
terms of net costs divided by net effects.
Two or more programs affecting the same health
outcome can be compared in terms of incremental
net costs of one program compared to
another, divided by incremental net effects of one
program compared to another.
Outcomes or effects included in cost-effectiveness
analysis can be defined narrowly or broadly,
although broad definitions are preferred
for decisions bearing on public policy.
Narrowly defined effects include those that
are intermediate in nature and that may be
easier to capture, such as immediate
increases in physical activity or decreases in blood
pressure associated with a hypertension intervention.
Broadly defined effects are those that are
more final and further removed, such as cases
of heart disease prevented,
lives saved, or years of life gained.
These broad outcomes are more appealing in terms
of effectiveness goals for a hypertension intervention.
However, you may only have intermediate outcomes
to work with unless you can follow intervention
participants over time or find good epidemiologic
evidence linking intermediate to final outcomes.
A major caveat in conducting cost-effectiveness
analysis is that outcomes in natural units
cannot be combined and
must be considered separately.
For example, a physical activity program
may have two intended effects: lowering blood
pressure and decreasing body mass index.
Because these two effects can’t be combined
in a cost-effectiveness analysis, the summary
measure for the analysis would be cost per
1 percent reduction in blood pressure and
cost per 1 percent decrease in body mass index.
However, the cost in these two summary measures
is the same, so the ratios are somewhat misleading.
This makes cost-effectiveness ratios
using natural units difficult for policy-makers
to translate.
One method for dealing with the problem of
multiple outcomes, particularly if there are
multiple health outcomes, is
to conduct a cost-utility analysis.
In this type of analysis, outcomes
are expressed as a health index.
This combines all health outcomes
associated with an intervention in terms of increases
in length of life and quality of life.
Length of life adjusted by quality of life
is known as a quality-adjusted life year,
sometimes referred to as a disability-adjusted life year.
In a cost-utility analysis, you could
compare interventions that affect different health
outcomes by using a quality-adjusted life
year—for example, when comparing interventions
that affect obesity, nutritional
outcomes, and cardiovascular disease.
The summary measure in a cost-utility
analysis is cost per quality-adjusted life year or
cost per disability-adjusted life year.
Cost-utility analysis is used when
quality of life, rather than length of life, is the
most important effect of the intervention.
For example, a cost-utility analysis of a
cardiac rehabilitation program might focus
on improved quality of life versus the
cardiac rehab’s influence on the length of life.
Cost-utility analysis is also used when the program
affects both morbidity and mortality outcomes.
An example is emergency medical services’
pre-hospital stroke care, which has long-term
effects on recovery and disability.
Cost-utility analysis can be used when
comparing interventions that affect different health
outcomes, like cancer versus
cardiovascular disease prevention.
Finally, cost-utility analysis should be used
when comparing results to other studies that
also employ cost-utility analysis as
the economic evaluation methodology.
Utilities, or preference weights, are a way
to quantitatively describe consumer preferences
for good quality of life and a subjective
measure of the usefulness that results from
being in different health states.
Because utilities are quantitative,
they are measurable and analyzable.
They’re typically based on a 0-to-1 scale,
where 0 is considered death and 1 is considered
perfect health.
To quantify benefits in a cost-utility analysis—
that is, to derive a quality-adjusted life year—you
need to know the intervention’s
effect on length of life and quality of life.
Data on length of life may be readily
accessible from epidemiologic literature.
Effects on quality of life, however, are
theoretically derived from individuals directly as their
preference weights, or utilities, for
the health state under consideration.
For example, what is the preference for having
a body mass index above 35 versus having one
between 25 and 35?
There are a number of ways to directly elicit utilities.
There are methods that rely on a specific
response method, such as scale versus choice,
and methods that rely on a specific type of
questioning format, such as asking about certain
events versus uncertain events.
Theoretically, to be considered an
economic “utility,” the response method must be
a choice and the questioning
format must include an uncertainty.
Therefore, the only correct way to derive
utilities for health states is the standard
gamble approach, although other
approaches are popular in the literature.
The standard gamble approach is based on the
conceptual framework for examining decisions
under uncertainty.
The respondent is given a choice between a
less-than-optimal health state— for example,
having a body mass index above 35—and a
lottery between two uncertain health states.
The two uncertain health states are often
perfect health and death and can be valued
as 1 and 0, respectively.
The two uncertain health states don’t
have to include perfect health and death.
The only requirement is that the certain health
state be in between the two outcomes associated
with the gamble.
In this setup, the respondent is asked something
like this: Imagine you have a body mass index
above 35, with no other adverse health outcomes.
Now suppose there’s a surgery available
to you that would reduce your body mass index
to a perfect level, thus giving you perfect health.
However, there’s a probability of
death associated with the surgery.
How low does the probability of death have
to be for you to be indifferent between your
certain health, with a body mass index
above 35, and the gamble of taking the surgery,
which could lead to death or perfect health?
The probability, or p value, derived from
this scenario reflects the utility for the
certain health state under consideration—
in this case, body mass index above 35.
Another way to directly elicit utilities is
the time trade-off method, which was developed
as an alternative to the standard gamble.
This method is used primarily in health research.
The respondent is offered a choice
between two alternatives of certainty.
The goal is to find the point where the person
becomes indifferent between the two alternatives.
Here’s the setup: Imagine that your remaining
life expectancy is 20 years and you have severe angina.
How much of your remaining life
expectancy would you give up to eliminate your severe
angina so that you have perfect health?
The number of years you would give up, divided
by the remaining life expectancy and subtracted
from 1, represents the utility
associated with severe angina.
Finally, the rating scale is the most
common approach to directly eliciting utilities.
This involves ranking alternatives
and then placing them on an ordinal scale.
For example, alternatives might include perfect
health, mild angina, severe angina, and death.
This example uses a visual analog
scale, which is typically horizontal.
There are a couple of advantages of this approach.
The cognitive burden is lower than with other
techniques, and people are familiar with the technique.
There are several disadvantages, however.
First is the anchoring effect.
What is set as the best possible state and
the worst possible state is subjective, creating
an indexing problem.
In addition, we can’t make any interpretations
about the numbers themselves, such as 88 versus
60, because of the ordinal scale.
Furthermore, people have an aversion to the
ends of the scale, so they treat the middle
of the scale as one scale and the
ends of the scale as another scale.
There are also context effects.
Ranking and scoring depend not just on
the states themselves, but also on the states
being compared.
Finally, this approach is based on
conditions of certainty and not really tied to utilities
or the theoretical foundation on
which cost-effectiveness analysis is based.
In addition to directly eliciting utilities,
there are published preference weights in
the literature from individual studies.
Compendia of weights are available
online at the Tufts Medical Center Web site.
The disadvantage of using weights
derived from other studies is comparability.
It could be that weights are derived from
different populations, for slightly different
health outcomes, et cetera.
As an alternative, there are widely available,
indirect elicitation tools that involve people
classifying their health states based on a
number of health domains—such as physical
functioning, role, social, and emotional—then
applying directly elicited preference weights.
Many such tools are
available, sometimes for a small fee.
The disadvantages of these tools are that
their weights may be derived from dissimilar
populations, they may not have included the
same health outcome you are considering, and
they may not have included the same severity levels.
The direct measures we discussed should be
elicited from general populations, but expert
panels or special
disease-specific samples are often used.
Major disadvantages are costs, time to
collect, and representativeness outside your study.
Here’s an example of an indirect utility
elicitation tool using the EuroQol 5 dimension
scale included in the Medical Expenditure
Panel Survey for a few years in the early 2000s.
Examples of decreases in utilities, or disutilities,
are shown for a number of chronic diseases.
Here’s how you can interpret these results:
Imagine a person with chronic hypertension,
with a remaining life expectancy of 20 years.
You could say that the person has a quality-adjusted
life expectancy of 19 years and 6 months—
or a loss of 6 months in quality-adjusted life expectancy.
This is derived by multiplying .025 by life
expectancy to get .5 years, or 6 months.
Once the utilities are determined for the
effects of the intervention, you can compare
the difference in quality of life and length
of life between the intervention and no intervention
in this example.
Here’s an example of a cost-effectiveness
analysis of the WISEWOMAN program.
The unit of effectiveness was reduction in
cardiovascular disease risk, which was then
translated, based on epidemiologic
evidence in the literature, to life years gained.
The uncertainty in the analysis was how
long the changes in the cardiovascular disease
risk were assumed to last, thus affecting
life years saved and costs per life years saved.
The program was assessed in relation to
itself, not compared to other interventions, which
produced an assessment of the
average cost-effectiveness ratio.
As a result, the authors found that the program
cost 4,400 dollars per life year gained under
the most optimal assumptions, which
would be changes in cardiovascular disease risk
assumed to last a lifetime.
But when more realistic, longer-term outcomes
were evaluated, the costs increased to 15,300
dollars per life year saved.
These changes in cardiovascular disease risk
were assumed to last for the lifetime of only
24 percent of the participants.
Costs were more than 133,000 dollars per life
year saved when the cardiovascular disease
risk changes were assumed to
last only one year and not longer.
This study shows the importance of having longer-term,
final outcomes in the cost-effectiveness analysis.
Unlike in benefit-cost analysis, where the
summary measures are objective, cost-effectiveness
analysis results in a subjective summary measure.
The policy-maker must still determine the
threshold below which an intervention is considered
cost-effective and above which an
intervention is not considered cost-effective.
Some arbitrary thresholds have been set in
both the United States and the United Kingdom,
but there is still some controversy about
these thresholds, particularly because the
cost-effectiveness ratios
haven’t been adjusted for inflation.
One way to determine threshold values is
to compare cost-effectiveness ratios to ratios
published in the literature or to ratios for
interventions that are commonly accepted as
good practice.
Here is an example from a study analyzing
the potential cost effectiveness of treating
all hypertension patients according to new
2014 guidelines, with CE ratios for each of
the different patient groups.
As you can see, the authors have defined
what they consider cost-effective for this study
and ranked each group
according to these defined cut-offs.
In this case, less than 50,000 dollars per
QALY was considered cost effective, which
is a commonly used threshold in the literature.
We might think of the cost-effectiveness
ratio on a continuum but without an actual rule
for policy-making.
There are some ranges within which an
intervention is clearly a good value, and other ranges
within which an intervention is clearly not.
It’s the intermediate cost-effectiveness
ratios that require some subjectivity on the
part of policy-makers.
Furthermore, a different set of standards
seems to apply to policy-making in the treatment
or clinical world compared to the
prevention or population-based health world.
But this discrepancy is due in part to the
newness of economic evaluations to prevention
and public health.
Much of the acceptance of economic evaluations
for informing policy-making and standardizing
practices comes from education modules
like this that introduce the concepts to the field.
Much has been done in public health
since the early 1990s, but there’s still
a long way to go.
Grosse and colleagues wrote a paper which
specifically discussed the use of economic
evaluations to inform public policy.
The authors found no consistent use of
economic evaluations to inform public policy in the
United States.
The same cannot be said in the United
Kingdom, where economic evaluations are part of how
the National Health Service
determines which benefits are covered.
The authors also found many missed opportunities
and no clear thresholds for cost-effectiveness
analysis in whatever policies
were informed by economic evaluations.
Disability-adjusted life years are another outcome
measure that can be used in cost-utility analysis.
Disability-adjusted life years were developed
in the international community primarily to
measure disease and injury burden and to allow
comparable estimates of these burden measures
across countries.
The disability-adjusted life year weights
are slightly different from the quality-adjusted
life year weights, with an inverted scale
of 0 referring to perfect health, or no disabilities,
and 1 referring to death, or 100 percent disabled.
Disability-adjusted life years are derived
from the estimates of years of life lost—which
is a common metric to measure burden of
disease internationally—and years of life lived
with a disability.
It’s essentially the same thing as
quality-adjusted life years in that life expectancy, in life
years, is adjusted for the
 number of years living with a disability.
Disability-adjusted life year weights are
derived differently than quality-adjusted
life year weights.
Instead of using the standard gamble, time
trade-off, or rating scale approaches with
a general population sample, experts
are asked to trade off numbers of people to keep alive
with certain conditions.
Here are the top ten disease categories
contributing to DALYs in the U.S. from 2010.
As would be expected, CVD is near the top
because of the high number of years of life
lost, as well as the decreased quality of
life it can cause for people living with the disease.
But even above CVD is neuropsychiatric
disorders; although these disorders don’t contribute
much to years of life lost from a disease,
the high number of years of life lived with
the disorders and the decreased quality
of life they cause pushes them to the top
contributor of DALYs.
In conclusion, economic evaluation is valuable
to decision-making and in setting health policy.
Economic evaluation is both art and science,
and it can be used to help prioritize resources
for the most effective strategies.
It assumes evidence and evidence-based decisions.
For researchers in public health and prevention,
this is an important component of program
evaluation that should be considered because
the demand for these evaluations is growing.
