e.g. staff costs, hospital operating costs, drug acquisition costs. These are generally easy to measure. Indirect costs relate to costs from a societal perspective, for example, the loss of earnings or costs borne by patients and their carers as a consequence of illness. These costs are more difficult to measure and are not always included in economic evaluations. Intangible costs relate to things like pain, worry and distress caused by illness; these are very difficult to quantify in monetary terms and are often only described rather than quantified in economic evaluations.
The last distinction to note is the importance of marginal costs, rather than average costs, in economic evaluation. An example to explain this concept is to envisage a new treatment that results in a patient being able to be discharged from hospital a day earlier than the current treatment allows. It might seem that the average cost of this bed day saved should be incorporated into the analysis but from an economist's point of view, the average cost would overstate the savings that arise. This is because, unless the hospital ward can be closed, the fixed costs (such as staff costs, heat and light) will still be incurred. The marginal cost of the bed day is a better reflection of the resource change brought about by the new treatment as this will take into account only the costs that change, for example, the savings in the patient's meals, drugs and perhaps some small savings in staff time.
Linking costs to benefits
To aid decision‐making, pharmacoeconomics aims to link the costs of a treatment to the benefits that it produces. There are four common types of economic evaluation, all of which use similar approaches to measuring the costs they include but which differ in the way that they treat the outcomes or consequences of treatment.
Cost‐minimisation analysis
Cost‐minimisation analysis (CMA) is the simplest form of economic evaluation. It is used where two or more treatments are known to have exactly the same outcomes, for example, in terms of proportion of patients successfully treated. The analysis is therefore simply reduced to a search for the least costly treatment. An example would be where a clinical trial had shown two antibiotic drugs to be equally effective in clearing an infection. The CMA would then simply compare the costs of the two treatments and the preferred treatment would be the lower cost alternative. The cost differences would include not only the drug acquisition costs but also any costs involved in drug administration, drug monitoring or management of adverse drug reactions.
Cost‐effectiveness analysis
The term cost‐effectiveness analysis (CEA) is often used loosely to refer to all types of pharmacoeconomic evaluation but does refer to a specific technique that compares alternative treatments in terms of a single natural unit which is common to both alternatives. It is of use where the alternative treatments achieve the outcome to different degrees, i.e. one treatment is more effective than the other. Treatments are then compared in terms of the cost per additional unit of benefit, for example, cost per life year saved, cost per ulcer healed or cost per extra mmHg blood pressure reduction. CEA is frequently used to compare interventions within a single disease area. For many decision makers, it has a significant limitation as it does not allow comparisons to be made between different areas of clinical practice with different outcomes (e.g. ulcer‐healing drug versus blood‐pressure lowering drug).
Cost–benefit analysis
In cost–benefit analysis (CBA) both the costs and the outcomes of alternative interventions are expressed in monetary units. This requires a financial value to be attached to the benefit of treatments, often by asking patients or the public to state how much they would be willing to pay for the effects of the intervention. In the CBA, treatments are judged to offer value for money if the cost of the treatment is less than the value placed on the benefits of the treatment. While an approach that has a good basis in economic theory, CBA can be a problematic form of analysis in healthcare because of the difficulties in asking people to place a notional monetary value on health benefits, and is encountered less frequently in pharmacoeconomics literature.
Cost–utility analysis
Cost–utility analysis (CUA) is a special form of CEA where alternative interventions are compared in terms of their impact on a single measure which tries to capture all the benefits (and adverse effects) of the intervention in question. The cost per quality‐adjusted life year (QALY), a measure that combines the impact of both the additional length of life and quality of life available from a treatment, is the most commonly used measure in CUA.
It is useful to have a working knowledge of CUA in today's healthcare environment, because it is the preferred method of economic evaluation of organisations such as NICE, SMC and other health technology assessment (HTA) agencies worldwide. CUA has been widely adopted, largely because it can be used to compare the cost‐effectiveness of the many different types of interventions that HTA organisations must assess.
Why use CUA and QALYs?
It is helpful to use an example to explain why CUA is such a useful form of economic evaluation.
In cardiovascular disease, there are many different treatments available and each treatment will give rise to a specific pattern of costs and effects. For example, in seeking to manage a patient's weight, clinicians could use lifestyle interventions or drug therapy. If drug therapy provided greater benefits but at an increased cost compared with lifestyle modification, then economic evaluation can provide a marker to help the decision maker judge whether the additional costs are justified by the additional benefits. In comparing two (or more) treatment options in an economic evaluation, the convention is to calculate the additional benefit provided by the more effective treatment and then present the findings in terms of ‘cost per additional unit of benefit’, e.g. ‘cost per additional life year gained’. Frequently, treatments offer a range of effects such as an improvement in symptoms and better survival. CEA using an outcome measure such as a ‘cost per additional life year gained’ would struggle to take account of both dimensions of benefit.
QALYs have the advantage of being able to capture gains in quality of life and quantity of life (=survival) in a single measure. The QALY adjusts length of life for quality of life by assigning a value (known as a utility value) between 0 and 1 (where 0 represents death or health states considered as bad as being dead and 1 represents perfect health) for each year of life. Negative utility values are also possible for some conditions that are considered to be worse than being dead, such as the end stage of a degenerative illness. Figure 3.1 illustrates the concept of a QALY. Without the new health technology, the individual's quality of life, measured on the y axis, would deteriorate over time until they die (‘Death 1’). However, with the new technology, the individual's quality of life is maintained at a higher level until they die, a few years later than they would have done with the original treatment (‘Death 2’). Area A represents the quality of life gain with the new treatment while area B corresponds to the survival gain with the treatment. Added together, they represent the total QALY gain associated with the new treatment.
Figure 3.1 The concept of quality‐adjusted life year (QALY).
How are the constituent parts of QALYs estimated?
Estimating the survival component of the QALY is often relatively straightforward as it can be taken directly
