Health economicswhat the nephrologist should know
Andrew J. Palmer
CORE Center for Outcomes Research, Basel/Binningen, Switzerland
Correspondence and offprint requests to: Andrew J. Palmer, CORE Center for Outcomes Research, Bundtenmattstrasse 40, 4102 Basel/Binningen, Switzerland.
Keywords: cost-effectiveness; costs; health economics; modelling; nephrology; thresholds; willingness to pay
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Background
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Clinical guidelines are increasingly influenced by health economic considerations as health policy decision makers look to make optimal use of limited healthcare budgets. The discipline of health economics has become an important component of healthcare systems in many countries worldwide and its influence on health policy decision-making globally is expanding. Just as a push towards evidence-based medical practice made it necessary for clinicians to become familiar with clinical trial terminology, the increasing use of health economic evaluations by health authorities, and their growing presence in mainstream medical journals, has made it important also to become familiar with the methodology and terminology of health economics. The purpose of this editorial is to briefly introduce some of the important principles and methodologies of health economics, particularly cost-effectiveness analyses.
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What is cost-effectiveness analysis and what clinical outcomes and costs are measured?
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New treatments and health interventions commonly offer potential benefits, but usually lead to added costs. Cost-effectiveness analysis allows us to examine the balance between the additional health benefits that come with a particular intervention and the costs associated with achieving those benefits. Cost-effectiveness and cost-utility studies are the most commonly published type of health economic investigation. Often the terms cost-effectiveness and cost-utility are used interchangeably, but strictly speaking, cost-effectiveness studies compare costs with clinical outcomes measured in natural units like life expectancy or years of disease avoided, whereas cost-utility studies compare costs with quality of life (utility)-adjusted outcomes like quality-adjusted life years (QALYs). QALYs reflect patient preference for certain states of health and have the advantage of allowing a direct comparison of a common unit across different fields of medicine. Quality-adjusted life expectancy is calculated by multiplying the number of years spent in a certain state of health with that health state's score. A score (or utility) of 1 reflects perfect health whereas a score of 0 indicates death. So, for example, a person who lives for 5 years in a state of perfect health (utility = 1) followed by 5 years in a state of health with utility of 0.5, then dies, had a life expectancy at baseline of 10 years, but a quality-adjusted life expectancy of (5 x 1) + (5 x 0.5) = 7.5 QALYs.
Most commonly, health economic evaluations take into account only direct medical costs which represent the value of all resources consumed whilst providing a medical intervention, including the costs of any medications, investigations, procedures, consultations, side effects, complications and management of worsening disease states. Another approach is to take a societal perspective and include indirect costs (costs due to lost productivity as a result of absence from work due to sick leave, premature death or early retirement). The choice to include indirect costs depends on who is paying for a new interventiona government-funded health insurance system that is financed from the same source as the social security system may want to have indirect costs included in any health economics studies presented to them, whereas a private health insurance company is generally only interested in the direct medical cost perspective.
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How are clinical and costs data brought together?
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Clinical inputs and cost data are brought together using two main methods. The first is randomized controlled trials (RCTs), which collect both clinical and cost data simultaneously, often referred to as piggy-back trials, as costs collection is added on to the back of the clinical study. Whilst RCTs are recognized as a good way to assess the efficacy of an intervention, they can be expensive to conduct, they are selective of the patients included and, coupled with a relatively short time horizon, they may not provide ideal data inputs for health economic studies. Meta-analyses can provide useful aggregate clinical data inputs, but these often incorporate results of RCTs, they retain some of the underlying problems associated with translation of efficacy results to effectiveness expected in real life populations. Data derived from epidemiological surveys, phase 4 studies, patient registries and other real life population studies may provide more realistic clinical input for health economic analyses.
However, since health economists tend to be interested in more general and often long-term measures such as QALYs rather than, for example, 30-day mortality, they frequently employ mathematical computer simulation models to extrapolate short- or medium-term data to a substantially longer timeframe, up to a lifetime horizon. The data used in the modelling process can be obtained from a variety of sources including RCTs, epidemiological studies, meta-analyses, and published material from both the private and public sectors. Modelling allows the integration of clinical inputs and costs into a logical framework, which permits a projection of outcomes of interest to healthcare decision makers such as QALYs and overall long-term costs. Models can be fairly straightforward or quite complex depending on the nature of the disease and the clinical, epidemiological, public health and costs data available. Diabetes, for example, requires relatively complex modelling because it has numerous interacting complications, various states of health and variable progression rates that are influenced by a multitude of factors. Chronic, progressive diseases such as renal disease associated with diabetes and/or hypertension are commonly reproduced using a Markov modeling approach, a well recognized and accepted technique [1]. Markov models are based on mutually exclusive health states from which a subject can be moved to or from over a period of time, according to transition probabilities determined by clinical evidence. Multiple interacting states can be added to Markov models by the inclusion of Monte Carlo simulation and tracker variables to create a more realistic simulation environment that closely represents the interactions between risk factors, development of complications and patient outcomes [2,3].
Recent examples of models that project long-term outcomes in patients with nephropathy (in multiple country-specific settings) by projecting relatively short-term clinical trial results have been reviewed in an accompanying article in this issue [4]. In these studies, a Markov modelling approach was used to project data from the 2.6-year irbesartan in Diabetic Nephropathy Trial (IDNT) [5] to time horizons of between 3 and 25 years.
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When is an intervention cost-effective ?
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The term cost-effective has a very specific meaning in health economics. Most new interventions are more effective, but more expensive than the current standard of care. In these cases, the incremental cost-effectiveness ratio (ICER) is widely used as a measure of cost-effectiveness. An intervention is considered cost-effective if the ICER, compared to a current gold standard treatment, falls below a given willingness to pay (WTP) threshold specified for each country. The ICER examines the balance between the higher costs of a new intervention versus the improved effectiveness of a new intervention. It is calculated as the difference in their mean costs (new comparator) divided by the difference in their mean effectiveness (new comparator), i.e. ICER = mean incremental costs/mean incremental effectiveness. WTP refers to the amount a payer is willing to pay for an improvement in patient outcome, and represents a threshold above which it is unlikely that a new intervention would be regarded as good value for money (and would be unlikely to receive reimbursement). Typically, WTP thresholds are expressed as costs per QALY. In the UK, for example, a WTP threshold per QALY gained that is often quoted as around £30 000 [6]. Other examples of country-specific thresholds are:
$US 93 500/QALY in the US,
$US 51 000/QALY in Australia and $US 83 900/QALY in Canada [6]. Interventions that exceed these thresholds do not represent good value for money and may be rejected by reimbursement decision makers.
Plotting incremental costs versus incremental effectiveness helps to visually display the ICER of one intervention versus another and allows a comparison with the WTP. In Figure 1, the vertical axis represents the difference in costs, the horizontal axis represents the difference in effectiveness, with the origin representing the current standard of care which is usually compared to a new intervention. The ICER is plotted in one of four quadrants. If the ICER falls in the upper left quadrant, it is more costly and less effective than the current standard of care, and would therefore be rejected. An intervention with an ICER that is both less costly and more effective (falling in the lower right quadrant) is said to be dominant to the current standard therapy, and therefore would usually be the preferred intervention. Interventions in the lower left quadrant are both less costly and less effective (a rare occurrence), and would be considered to be of questionable value. When the ICER falls in the upper right quadrant, with improved effectiveness but higher costs, the cost-effectiveness must be assessed. If the ICER is plotted relative to a line representing the WTP and it falls below the WTP line, it would be considered good value for money, but if it falls above the WTP line, it would be regarded as too expensive when the magnitude of the clinical improvement is taken into consideration.
For example, as outlined in the Palmer et al. [4] review article in this issue, projection of data from the IDNT predicted that irbesartan would increase life expectancy and decrease costs of care compared to a control treatment arm in hypertensive patients with type 2 diabetes and overt nephropathy. Compared to control, irbesartan would fall in the lower right quadrant of Figure 1 and be considered dominant. On the other hand, the ICER for screening for lung cancer with helical computed tomography in older adult smokers vs no such screening has been estimated to be $116 300 per QALY in the US [7] and was deemed not to be cost-effective. When treatment of multiple sclerosis with beta interferon was compared to standard treatment in the UK, the ICER was £187 000 and it was also considered not to represent good value for money [8]. In the analysis by Kalish et al. [9], tPA was found to have a higher cost but greater effectiveness than streptokinase. The situation might arise, however, where a decision maker does not have the financial resources to fund tPA and, rather than have no thrombolytic therapy, would chose streptokinase rather than tPA, whilst accepting the inferior patient outcomes. In sharp contrast, hospital dialysis for end-stage renal disease was found to be cost-effective in a US setting when compared to continuous ambulatory peritoneal dialysis (ICER $74 000) [10].
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Can we trust the results of modelling studies?
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In general, models used to generate health economic outcomes can only be as robust as the clinical and epidemiological data on which they are based. Clinicians must turn a critical eye to the data used in the model, and draw their own conclusions about the quality of the input data. There is an inherent uncertainty in projecting clinical observations made in studies lasting 310 years over time horizons of 20 years or more. To limit the scepticism surrounding modelling results, it is vitally important that models are transparent, validated and account for input uncertainty.
In any cost-effectiveness study, sensitivity analyses should be performed by varying input variables to assess the extent to which a model's outcomes are affected by input uncertainty, and to identify the key drivers of clinical and cost outcomes. If key parameters are varied through plausible ranges, and the conclusions do not change as a result, this should improve our confidence in the conclusions.
Whilst assumptions do need to be made, and reality is often simplified, in the absence of long-term clinical data, modelling can generate valuable information for the clinician and health economist alike that is simply not available from long-term clinical or epidemiological trials.
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Conclusion
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Cost-effectiveness analyses are being more frequently and widely published. Many decision-making bodies request submission of health economic dossiers to support reimbursement applications. Clinicians need to be familiar with cost-effective analysis methodology and terminology, and be able to interpret the ever-increasing number of health economic analyses that are reported in clinical journals.
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Acknowledgments
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Dr Palmer would like to thank Drs Dan Tucker and William Valentine, CORE Center for Outcomes Research, for their invaluable assistance in the preparation of this manuscript.
Conflict of interest statement. Dr Palmer is employed by CORE Center for Outcomes Research, which has received unrestricted research grants and consulting fees from Amgen, Amylin, Bristol-Myers Squibb, Coloplast, Eli Lilly, Jansen, Medtronic, Merck-Santé, Novartis, Novo Nordisk and Sanofi-Aventis.
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