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REVIEW ARTICLE
Cost-effectiveness Analysis in Clinical Practice
The Case of Heart Failure
Michael W. Rich, MD; Robert F. Nease, PhD
H
eart failure is the leading cause of hospitalization in adults older than 65 years, and it is
currently the most costly cardiovascular disorder in the United States, with estimated
annual expenditures in excess of $20 billion. Recent studies have shown that selected
pharmacological agents, behavioral interventions, and surgical therapies are associated
with improved clinical outcomes in patients with heart failure, but the cost implications of these diverse treatment modalities are not widely appreciated. In this review, a brief outline of costeffectiveness analysis is provided, and current data on the cost-effectiveness of specific approaches to
managing heart failure are discussed. Available evidence indicates that angiotensin converting enzyme inhibitors, other vasodilators, digoxin, carvedilol, multidisciplinary heart failure management
teams, and heart transplantation are all cost-effective approaches to treating heart failure; moreover,
some of these interventions may result in net cost savings. Arch Intern Med. 1999;159:1690-1700
Heart failure affects an estimated 4.9 million Americans, and approximately
400 000 new cases are diagnosed each
year.1,2 In 1995, there were 872 000 hospital admissions attributed primarily to
heart failure, and there were an additional 1.8 million admissions with heart
failure as a secondary diagnosis.2,3 Approximately 80% of all heart failure admissions occur in individuals older than
65 years, and one fifth of all admissions
in that age group have a primary or secondary diagnosis of heart failure.2,3 As a
result, heart failure is the leading indication for hospitalization in older adults.1-3
From 1980 through 1993, the number of physician office visits for heart failure increased by 71%, from 1.7 million to
2.9 million annually.2 In addition, more
than 65 000 patients with heart failure receive home health care each year.2 Moreover, in 1995, heart failure was listed as
the primary cause of death in more than
43 000 cases and as a contributory cause
in an additional 220 000 cases,1,2 and more
than 90% of heart failure deaths occurred in patients older than 65 years.4
From the Geriatric Cardiology Program and the Division of General Medical Sciences,
Barnes-Jewish Hospital, Washington University School of Medicine, St Louis, Mo.
Because of its high prevalence and associated high medical resource consumption, heart failure is now the single most
costly cardiovascular illness in the United
States, with total costs for 1998 estimated at $20.2 billion.1 Remarkably, heart
failure hospitalization costs in 1991 exceeded those for all cancers and all myocardial infarctions combined.5 Moreover,
in contrast to recent declines in ageadjusted mortality rates from coronary
heart disease and hypertensive cardiovascular disease,6,7 the incidence and prevalence of heart failure are increasing, largely
owing to the aging of the population.8 As
a result, the costs of caring for patients with
heart failure are expected to escalate well
into the 21st century.
For these reasons, the last 2 decades
have witnessed a remarkable explosion in
heart failure research, and many new therapeutic options are now available. In addition, there has been considerable interest in
defining the costs associated with heart failure management and identifying those interventions that are most efficacious from
both the clinical and cost perspectives. In
this review, a brief discussion of costeffectiveness analysis is provided, followed by a summary of currently available
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data relevant to the costs of treating
patients with heart failure.
COST-EFFECTIVENESS
ANALYSIS
The goal of cost-effectiveness analysis is to estimate the monetary
cost required to achieve a gain in
health benefit. More specifically,
cost-effectiveness analysis estimates the incremental cost
required to improve a selected
clinical outcome by 1 unit (eg, cost
per year of life saved, cost per
stroke prevented).9,10 The goal is
not to define the greatest benefit at
the lowest cost, since in most cases
it will not be possible to achieve
both simultaneously.
Calculating the cost-effectiveness ratio requires estimating
the change in cost associated with a
given intervention (ie, the numerator of the ratio), as well as the
change in health benefit provided
by that intervention (ie, the
denominator of the ratio). The
notion of incremental costs and
incremental benefits is crucial for
cost-effectiveness analysis. Thus,
the question posed is often of the
form: “What is the monetary cost
of moving from intervention X to
intervention Y in relation to the
associated change in health benefit
in moving from X to Y?”
In estimating the cost-effectiveness ratio, cost is typically measured in dollars. Health benefit, however, may be expressed in a variety
of ways. In cost-benefit analysis, both
the costs and benefits are expressed in monetary terms (ie, the
cost outlay is compared with the
monetary value of the benefits obtained).11 Because it is often difficult to place a monetary value on a
clinical benefit (eg, how much is 1
year of life or 1 less stroke worth?),
cost-benefit analysis is used infrequently in the medical arena. When
a study measures health benefit in
disease-specific terms (eg, strokes
prevented), the method is referred
to generically as cost-effectiveness
analysis. Although measuring health
benefit in disease-specific terms may
be helpful in comparing interventions for a specific condition, it is less
useful for comparing interventions
across diseases. For example, it is un-
clear whether preventing 1 stroke at
a cost of $10 000 is better or worse
than preventing 1 hip fracture at a
cost of $5000.
To facilitate comparisons
across diseases, analysts often measure health benefit as the gain
in quality-adjusted life years
(QALYs).12,13 Quality-adjusted life
years are designed to capture the
effects of an intervention on both
length and quality of life. Specifically, time spent in less than ideal
health is adjusted downward. The
degree of adjustment is determined
by the utility for that health state.
If, for example, a patient with heart
failure equates 2 years of life at his
or her present health state with 1
year of life at ideal health, then the
utility for that individual’s present
health state is 0.5 (ie, 1 year of ideal
health is worth 2 years of present
health). In other words, each year
of life at the present health state is
equivalent to 0.5 QALY. The costutility ratio, defined as the cost
required to gain 1 QALY, permits
cost comparisons to be made across
a wide range of interventions and
diseases. Specific methods have
been developed for assessing utilities, and the reader is referred to
other sources for additional details
and examples.14
Although there are several
ways of categorizing costs, the total
costs associated with a specific
m e d ical illness or condition
include 3 major components:
direct costs, indirect costs, and
intangible costs.14,15 Direct costs
encompass the actual costs of services rendered, including hospitalization costs, diagnostic tests and
procedures, medications, office visits, and rehabilitation costs. Indirect costs include loss of income as
a result of illness, travel expenses,
and costs for specialized services,
such as meals-on-wheels and adult
day care. Intangible costs include
the nonquantifiable costs associated with physical and emotional
pain and suffering. Although some
cost-utility analyses attempt to
include these factors in assessing
health benefit, most published cost
analyses include only direct costs,
and these are often limited to hospital costs or some other component of the total direct costs.
Cost vs Charge
The term cost refers to the actual or
true costs associated with providing a service. Unfortunately, the
true costs are often difficult to
determine, since they may include
such diverse resources as personnel, space, equipment, depreciation, and shared goods (eg, electricity and telephone). For this
reason, charges are often used as a
surrogate for costs. However,
charges do not necessarily reflect
true costs in any consistent fashion. For example, the charge for
performing a specific procedure is
often fixed, whereas the cost is
dependent on several factors,
including procedure volume (ie,
the cost per case is lower if 10
echocardiograms are performed
per day than if only 1 is performed). In an effort to overcome
this and other limitations, a costto-charge ratio is often calculated.16
This ratio is based on estimated
true costs and charges at a given
institution, and it is therefore
facility-specific. On the other hand,
in most cases the cost-to-charge
ratio is not based on specific procedures or diagnoses, and for this
reason it may not provide a valid
method for estimating costs.
Another approach to estimating costs is through reimbursement
or collections data. Under the Medicare Prospective Payment System,
hospitals receive a predetermined
amount of money for each hospitalization, and this amount is based primarily on the discharge diagnosis category (diagnosis related group
[DRG]). The reimbursement schedule, which is designed to reflect average costs adjusted for region and comorbidity, provides a simple method
for gauging hospitalization costs. Unfortunately, DRG reimbursement may
not reflect actual costs at a given institution or for an individual patient.
Discounting
In performing cost-effectiveness
analyses, it is often important to determine the time frame during which
costs and benefits accrue, since the
current value of benefits to be
achieved in the future is less than the
value of the same benefits achieved
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Sensitivity Analysis
In most cost-effectiveness analyses,
a series of assumptions are made concerning both costs and outcomes. For
example, the average cost of intervention X may be estimated at
$10 000, the health benefit may be assumed to be the prevention of 1
stroke for each 50 patients treated,
and the risk of major complications
may be estimated at 2%. Not surprisingly, the calculated incremental costeffectiveness ratio may vary considerably depending on the validity of
the baseline assumptions. Sensitivity analysis assesses the impact on the
cost-effectiveness ratio of varying the
baseline assumptions across a range
of clinically plausible values.9,10 Sensitivity analysis thus provides insight into the stability of the costeffectiveness ratio, identifies those
baseline assumptions that have the
greatest impact on overall costs, and
defines boundaries beyond which a
specific intervention may no longer
be cost-effective (eg, if the reduction in mortality is less than 10% or
if the complication rate exceeds 5%).
Interpretation of
Cost-effectiveness Analyses
In comparing 2 treatment strategies
using cost-effectiveness analysis, 1 of
4 results may occur (Figure). The
first situation (quadrant I) occurs
when the new intervention is both
more effective (eg, saves more lives,
prevents more strokes) and less expensive than standard treatment. In
this case, the new intervention is said
to dominate, and it is clearly costeffective. In a second scenario, the
new intervention may be both less effective and more costly than standard treatment (quadrant III). This
is the opposite of the first possibility, and in this case the standard treatment dominates. When 1 intervention dominates another, interpreting
the analysis is straightforward. Unfortunately, such dominance occurs
infrequently in the clinical setting.
The third possibility occurs
when the intervention is less effective and also less costly (quadrant II).
This possibility presents a dilemma,
since there may be situations where
the less effective and less expensive
treatment is actually more costeffective. Depending on resource
availability, the less costly therapy
may represent the best clinical option. The fourth possibility (quadrant IV) occurs when the new therapy
is both more effective and more expensive (eg, tissue plasminogen activator compared with streptokinase
for acute myocardial infarction). In
this last situation, which occurs commonly, as well as in scenario 3, the
cost-effectiveness ratio can provide
guidance as to the relative merits of
the 2 interventions. Specifically, the
incremental cost-effectiveness ratio
(dollars per year of life gained) or the
incremental cost-utility ratio (dollars per QALY gained) expresses the
relative efficiency of the 2 interventions in producing health benefits.
What constitutes a cost-effective intervention? Clearly, any
new treatment that reduces costs
without compromising efficacy is
cost-saving and therefore costeffective. Renal dialysis is a common benchmark used to assess the
cost-effectiveness of interventions
that are both more effective and
more costly. Renal dialysis is estimated to cost approximately $40 000
for each year of life gained. Alternatively, Goldman et al17 have suggested that an incremental costeffectiveness ratio of less than
$20 000 per QALY is very attractive, a ratio of $20 000 to $60 000 per
QALY is acceptable, a ratio of
$60 000 to $100 000 per QALY is
higher than currently accepted standards, and a ratio in excess of
$100 000 per QALY is unattractive.
However, since the incremental costeffectiveness ratio involves a tradeoff
between dollars spent and health
benefits gained, the ranges suggested by Goldman et al (or by any
arbitrary set of thresholds used for
decision making) reflect society’s
current willingness to pay for a specific benefit, and these ranges are
therefore a matter of public policy
+
III
IV
Effectiveness
–
II
+
I
–
Possible outcomes of cost-effectiveness
analysis (explained in the “Interpretation of
Cost-effectiveness Analyses” section).
rather than a scientifically based assessment of true cost-effectiveness.
In evaluating the results of costeffectiveness analysis, several additional factors should be considered.
Did the analysis compare 2 potentially effective interventions, or was
a single intervention compared with
placebo? It is often easier to demonstrate cost-effectiveness when the new
treatment is compared with no
therapy. Was the analysis based on
costs or charges? Because charges
typically exceed costs, analyses based
on charges will tend to overestimate
the true cost-effectiveness ratio. Was
the population studied representative of clinical practice? If the study
sample was highly selected, the results of the analysis may not be applicable to the general population.
What was the time horizon for the
analysis? Although data are often
available only for the near term,
health benefits may be long-lasting,
and this should be factored into the
analysis. Were costs and health benefits appropriately discounted? If not,
the true cost-effectiveness could be
either overestimated or underestimated. How were the benefits measured, and was quality of life taken
into consideration? Did the study
evaluate all costs, or was it limited to
direct costs or to an even smaller
component of total costs (eg, hospitalization costs)? The nature of the
cost analysis can have a profound effect on the study’s implications. For
example, a new intervention may
have a favorable effect on stroke survival without increasing hospital
costs, and such an intervention would
therefore appear to be cost-effective. However, if neurological func-
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Cost
today. Thus, an individual would be
willing to pay more today to prevent a stroke tomorrow than to prevent a stroke 10 years from now.
Cost-effectiveness analysis accounts for the time value of both
money and health benefits by discounting future value, usually at a
fixed rate (eg, 3%/year).9,10
tion is not improved, and if survivors require prolonged rehabilitation
and nursing home care, the overall
cost-effectiveness of the intervention may be greatly compromised.
Limitations
Although cost-effectiveness analysis
provides a useful tool for evaluating
therapeutic strategies and developing treatment and reimbursement
policies, certain methodological limitations must be recognized. First,
well-designed randomized controlled trials with prospectively collected cost data that directly measure the effect of a specific
intervention on an identified outcome are exceedingly uncommon.
This lack of direct evidence mandates the use of sophisticated modeling techniques that often combine
data from a variety of sources and rely
on expert judgment to estimate clinical outcomes and related costs. These
estimates, which are often based on
a series of assumptions, may or may
not accurately reflect true costs and
benefits. Although sensitivity analysis provides a method for evaluating
the robustness of the model, the quantitative outcome of the analysis may
nonetheless communicate a level of
precision that is unwarranted.
A second limitation relates to
the generalizability of a specific
analysis to routine clinical practice. Cost-effectiveness analyses are
often based on data from clinical trials that may involve a highly selected patient population treated in
a specific practice environment (eg,
an academic medical center) for a
fixed period. Clearly, the results of
these analyses may not be directly
applicable to other patient populations, practice settings, or time horizons. In addition, analysts may use
different methods and assumptions in developing cost-effectiveness models, and these differences
may substantially influence the results. For these reasons, care should
be taken both in comparing the results of different cost-effectiveness
analyses and in applying the results to clinical practice.
A third limitation concerns the
inability to measure intangible costs
and the related difficulty of accurately quantifying quality of life. Both
of these factors may serve to limit the
validity of cost-effectiveness analyses in general and cost-utility analyses in particular.
Despite these limitations, costeffectiveness analysis offers a unique
means to generate insights into the
costs and benefits associated with
therapeutic interventions, for which
the outcomes are often complex, dynamic, and uncertain.
Clinical Implications
Cost-effectiveness analysis may be
used to compare costs associated
with selected interventions when total resources are limited. For example, in choosing between 2 new
and unrelated programs, both of
which would cost $100 000 per year
to operate, policy makers would
have an apparently easy choice if 1
program spent $20 000 per QALY
gained, while the other spent
$200 000 per QALY gained. Without a cost-utility analysis, the relative clinical merits of the 2 programs may be less apparent.
At the level of the individual
practitioner, however, the situation
is much more complex. Physicians
are appropriately concerned with providing each individual patient with
the best possible care. Although cost
may come into play, it is not and
should not be the overriding concern.
It cannot be expected, for example,
that physician A will voluntarily withhold treatment X from a given patient
becauseofthetheoreticalconcernthat
administering such treatment will
mean that physician B will not be able
to give treatment Y (ie, a more costeffective therapy) to another patient.
Despite these difficulties, costeffectiveness analysis is increasingly
being used to guide policy and influence medical decision making. It is
therefore appropriate for physicians
to have a working knowledge of costeffectiveness analysis and its pitfalls.
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