Fwd: NATAP: Liver Transplantation, cost-effectiveness

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Liver transplantation is effective, but is it cost-effective?

Editorial

Liver Transplanation, December 2003, Volume 9, Number 12

Kiran Bambha

W. Ray Kim

From the Division of Gastroenterology and Hepatology, Mayo Clinic and

Foundation, Rochester, MN.

In 1983, The National Institutes of Health released a consensus conference

statement that endorsed liver transplantation as an effective nonexperimental

treatment for end-stage liver disease. Now, two decades later, liver

transplantation has become a widely accepted therapy for improving survival and

quality

of life for patients with either acute or chronic end-stage liver disease. The

outcome of liver transplantation has improved steadily as donor and recipient

selection criteria have been refined, new surgical techniques have been

developed, and newer immunosuppressive regimens have been introduced. Current 1-

and

5-year post-liver transplantation survival rates in the United States are

approximately 85% and 75%, respectively.

However, for logistic and ethical reasons, there have been no randomized

controlled trials directly comparing liver transplantation with conservative

therapy. The success of liver transplantation has been measured by comparing

observed posttransplantation survival with the predicted natural history by

using

validated disease-specific prognostic models. Primary biliary cirrhosis (PBC),

primary sclerosing cholangitis (PSC), and alcoholic liver disease (ALD) are

three chronic hepatic conditions for which there exist well-validated natural

history models. Studies comparing posttransplantation survival with predicted

survival for these three liver diseases have shown 1- and 5-year survival rates

of 83% and 78% for PBC, 94% and 86% for PSC, and 84% and 72% for ALD,

respectively. Data from these and similar studies showed that liver

transplantation is

an effective therapy for prolonging survival in patients with end-stage liver

disease.

Although the effectiveness of liver transplantation in improving survival of

patients with end-stage liver disease is evidenced in the medical literature,

much fewer data are available on the cost-effectiveness of the procedure. The

underlying health economic issue that analyses of cost-effectiveness are

designed to help address is, “What allocation of resources will achieve the

greatest gain in health attainable without exceeding the allotted budget?†One

approach to this issue is to calculate the cost-effectiveness ratio, which, with

regard to liver transplantation, is costs associated with liver transplantation

(expressed in monetary units) divided by the effectiveness of liver

transplantation (measured, for example, in years of survival). However, for

results of a

cost-effectiveness analysis to be truly informative, a medical intervention

should always be evaluated in light of the alternative interventions with which

it is competing. Therefore, the incremental cost-effectiveness ratio may be

calculated as the difference in costs between the two competing interventions

divided by the difference in effectiveness between the two.

As shown by Longworth et al in their report, “Midterm cost-effectiveness of

the liver transplantation program of England and Wales for three disease

groups,†in this issue of Liver Transplantation, an economic comparison is

made

between performing liver transplantation for patients with end-stage liver

disease

and taking a conservative medical approach toward treatment of these pat

ients. The investigators use their directly observed data for costs and outcomes

for a group of patients with PBC, PSC, and ALD who underwent liver

transplantation. Because there have been no randomized controlled trials

directly comparing

liver transplantation with conservative management for end-stage liver

disease, costs and outcomes data for the comparator group (i.e., absence of

liver

transplantation) were estimated by using validated disease-specific models for

PBC, PSC, and ALD that predict the natural history of disease in the absence of

intervention. Thus, for each patient included in this cost-effectiveness

analysis of liver transplantation, data reflecting a patient's observed

transplantation costs and outcomes were included, along with estimations for

each

patient of what their costs and outcomes would have been in the absence of liver

transplantation (shadow costs and shadow outcomes).

There are many factors that must be included in a cost-effectiveness

analysis. Accurate assessment of costs of the medical intervention being

considered is

critical. Costs are derived from multiple sources and may be direct (i.e.,

hospital facilities, personnel, laboratory, medication, imaging, and procedural

costs) or indirect (costs associated with lost wages or lost productivity).

Although the concept of costs needed to produce these health services is

relatively straightforward, determining costs accurately can be difficult in

real

life. For example, many economic analyses estimate costs from published charges

or payments for hospital care, physician services, laboratory testing,

medications, and other services, which often are not representative of true

economic

costs. In that regard, Longworth et al are commended for having undertaken a

resource-based estimation of costs associated with liver transplantation,

including inpatient and outpatient care, hospital overhead costs, medications,

testing, length of transplantation operation, and staff costs. A similar method

has

been used by Showstack et al17 in the United States. In the Longworth study,

information on transplant center resource utilization was collected

prospectively for each patient beginning at the time of initial assessment for

liver

transplantation. Additionally, despite the paucity of published data, the

investigators also attempted to include costs associated with the process of

organ

procurement in their analysis.

With regard to indirect costs, methodologic issues arise in trying to

estimate costs associated with loss of productivity because of illness or

premature

death. Although measurement of these costs is difficult and may be inaccurate,

their inclusion in a cost-effectiveness analysis generally is recommended. In

the report by Longworth et al, indirect costs associated with liver

transplantation and conservative management were not explicitly considered. This

was

caused in part by the perspective of this analysis, namely, that of the payer

(the British National Health Service). However, exclusion of indirect costs has

the net effect of underestimating the cost-effectiveness of liver

transplantation because indirect costs are likely to be greater in the

conservative

management strategy compared with liver transplantation.

The other critical component of a cost-effectiveness analysis is measurement

of the effectiveness of the medical interventions under consideration. A

variety of measurements of effectiveness may be used in a cost-effectiveness

analysis. These include years of life gained by the prevention or delay of death

because of the medical intervention, years of life lost because of side effects

or complications of the intervention, or quality-adjusted life-years (QALYs)

gained by the prevention or delay of morbid events because of the intervention.

QALYs consider the quality, as well as quantity, of life affected by the

medical intervention and currently are the preferred unit of effectiveness in

cost-effectiveness analyses. Although in theory, use of quality-adjusted

measurements is desirable, there are difficulties and limitations associated

with their

practical application, including how and in what population quality of life

should be measured.

Longworth et al expressed outcomes in terms of QALYs for patients who

underwent liver transplantation and for their derived comparator group.

Health-related quality of life was assessed by using the well-known validated

EuroQol EQ-5D

questionnaire, which was sent to patients at multiple times

pretransplantation and posttransplantation. Health-related quality of life

scores were valued

relative to scores for the UK general population. However, it is worthwhile to

note that only 48% to 76% of patients had usable EuroQol EQ-5D values at the

main times in this study, and missing scores for some patients were input.

Although inputting missing values is an accepted means for dealing with missing

data, this process introduces additional uncertainty into the analysis.

In addition to the two main components of a cost-effectiveness analysis,

namely, costs and effectiveness of the medical interventions, several other

factors need to be addressed in such an analysis. Three of these factors,

discounting, patient population, and time horizon, are discussed here because

they are

particularly relevant to the report by Longworth et al.

Discounting must be factored into any cost-effectiveness analysis, and it is

a basic principle of economics that the value of a dollar today is worth more

than the value of a dollar in the future. This is a reflection of our time

preference for money, meaning that the majority of people would prefer the same

amount of money today rather than some time in the future. Therefore, costs

occurring in the future should be valued less (or discounted). Discounting

adjusts future costs and expresses them in terms of their present value. This

gives

greater weight to costs the earlier they occur. In a cost-effectiveness

analysis, because outcomes of the medical intervention under consideration are

being

valued relative to costs and costs are being discounted, outcomes must also

be discounted.

It is interesting to note that Longworth et al used different discounting

rates for costs and outcomes (6% and 1.5%, respectively). The investigators

report that these discount rates are in accordance with National Health Service

guidelines 2001. Currently, for cost-effectiveness analyses conducted in the

United States, it generally is agreed that discount rates used for both costs

and

outcomes should be the same to avoid the problem of artificially inflating or

deflating the apparent cost-effectiveness of a medical intervention.18 In the

investigation by Longworth et al, the greater discount rate applied to costs

compared with effectiveness will have the effect of making liver

transplantation appear more cost-effective than it may be. Overall, the short

time horizon

(27 months) makes the bias caused by differential discounting between costs and

effectiveness rather small.

The two other factors that bear mentioning because they relate to this

cost-effectiveness analysis of liver transplantation conducted by Longworth et

al

are issues surrounding the patient population and time horizon. Characteristics

of the patient population used for a cost-effectiveness analysis will

influence results of the analysis in terms of quantity and quality of costs and

outcomes. It is important to keep in mind that there is an inherent selection

bias

underlying studies that focus primarily on patients listed for and/or

undergoing liver transplantation. These patients are a select group of people

who often

have less comorbidity and are predicted to benefit from transplantation.

Patients with significant comorbidities or who are otherwise deemed

inappropriate

liver transplantation candidates are not represented in these studies.

However, the natural history models, such as those developed for PBC and PSC,

are

based on relatively unselected populations of patients with these liver

diseases.

Thus, although comparisons between survival predictions based on these models

and observed survival in liver transplantation patient populations may

suggest a greater survival advantage with liver transplantation, it is important

to

bear in mind that at least some of this difference in survival may be

attributable to inherent differences that exist between the patient populations

(i.e.,

liver transplantation patients versus historic cohorts of patients with liver

disease).

With respect to the time horizon in this investigation by Longworth et al,

patients were followed up during a 27-month period. This included an average of

3 months of waiting list time, with 2 years of posttransplantation follow-up.

A long-term follow-up period would be ideal for such a therapy as liver

transplantation because net costs and effects associated with the procedure will

continue to change beyond the 2-year period. It is likely that the incremental

cost-effectiveness ratio of liver transplantation would decrease with time,

making liver transplantation appear cost-effective.

An important limitation of model-based (as opposed to trial-based)

cost-effectiveness analyses is that these analyses entail many assumptions and

estimations. The savvy reader needs to pay close attention to these assumptions

and the

evidence supporting them because, as we point out in this discussion of liver

transplantation, certain assumptions may significantly influence the reported

cost-effectiveness of a medical intervention. For example, in this report by

Longworth et al, several potential biases may be present that work in opposing

directions. There may be overinflation of the reported cost-effectiveness not

only because of the differential discounting applied to costs and

effectiveness in the study, but also because of the selection bias inherent in

studies of

transplantation patients, which influences observed patient outcomes.

Conversely, truncation of the patient follow-up time at 27 months has the likely

effect of making liver transplantation appear less cost-effective than it would

had the study been conducted with a longer time horizon. Despite these

uncertainties and difficulties, results of this analysis provide valuable

information

to continue to support liver transplantation and identify key determinants in

resource allocation and health care policy decision making.

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