9 Economic evaluation in obstetrics and gynaecology

9 Economic evaluation in obstetrics and gynaecology

9 Economic evaluation in obstetrics and gynaecology MARK SCULPHER This chapter describes the key elements of an economic evaluation, the different ty...

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9 Economic evaluation in obstetrics and gynaecology MARK SCULPHER

This chapter describes the key elements of an economic evaluation, the different types of study and the usefulness of the information each provides. The focus of most publications on the principles of evidence-based medicine is the individual patient presenting with a given condition. However, within heath care, there also needs to be a consideration of patients and other individuals at the group level. Economic evaluation of health-care interventions and programmes is focused on decision-making at the group level. The starting point of this sort of analysis is that, whatever the structure or financing arrangements, the resources available for a health-care system (e.g. skilled staff, equipment, ward space, etc.) are inherently limited, but the beneficial uses to which those resources could be put are unlimited. Out of this economic fact of life springs the key concept of 'oppoaunity cost', i.e. the benefits to patients that are forgone by using a resource in one way rather than its next best alternative. Hence choices have to be made about which services to provide in order to maximize patient benefit and to minimize opportunity cost, that is, decisions about how to maximize the efficiency of the health care system. Economic evaluation seeks to inform this decision-making process, addressing the following sorts of question. Is hysterectomy or transcervical endometrial resection a more etficient treatment for menorrhagia? Should the additional resources available to a healthcare commissioner be put into gynaecology or medical oncology or some other area of health care? Should the government deliver additional resources to provide extracorporeal membrane oxygenation (ECMO) to support term babies with severe respiratory failure? Increasingly, clinicians and other health service staff are having to contribute to this sort of decision-making. It is important, therefore, that they are able to understand and to appraise critically published economic evaluations. Using examples of published studies in the field of obstetrics and gynaecology, this chapter describes the key elements of a study, the different types of economic evaluation and the usefulness of the information they provide. Baitlikre's Clinical Obstetrics and Gynaecology-661 Vol. 10, No. 4, December 1996 ISBN 0-7020-2260-8 0950-3501/96/040661 + 15 $12.00/00

Copyright © 1996, by Baillibre Tindall All rights of reproduction in any form reserved



DEFINING E C O N O M I C EVALUATION Economic evaluation can be defined as 'the comparative analysis of alternative courses of action in terms of both their costs and consequences' (Drummond et al, 1987). There are two key elements contained within this definition. The first of these is the term 'comparative'; in the same way that clinical evaluation requires a comparison of two or more interventions, so too does any economic analysis aimed at generating information to assist with resource allocation. For example, it would not be possible to assess the efficiency of ECMO in newborns in isolation; there would have to be an assessment of the incremental costs and benefits of the technology compared with usual practice. The second key element in the definition is that economic evaluation is concerned not only with costs, but also with consequences for health and other things that patients value. These two key elements are central to the conditions that must exist for one therapy to be considered more efficient than another. For example, although there have been some small randomized trials comparing laparoscopic hysterectomy with the abdominal technique, no study has yet explored their relative efficiency. Figure I describes the conditions required for economic efficiency using the example of hysterectomy.

LH is more effective than AH and no more costly

LH is less costly than AH and no less effective

LH is more effective than AH and more costly, but the incremental resources necessary to provide LH could not be used to generate a greater number of benefits elsewhere

LH is less effective than AH and less costly, and the incremental resources necessary to provide AH could be used to generate a greater number of benefits elsewhere

Figure 1. Four possible conditions that would ensure that laparoscopic hysterectomy (LH) is more efficient than abdominal hysterectomy (AH).

KEY CONCEPTS IN E C O N O M I C EVALUATION This section introduces some of the important concepts in economic evaluation.

Estimating costs In estimating the differential cost of two or more interventions, it is important at the outset to decide the perspective to be taken, i.e. costs to whom? Table 1 illustrates the spectrum of perspectives that can be adopted, starting from the narrow, perhaps focusing on one budget, and progressing to the societal, which, in principle, considers costs to all individuals and organizations. Whatever the perspective, all costing exercises can be seen as a two-stage process. The first stage involves estimating the type and number of physical


ECONOMIC EVALUATION IN OBSTETRICS AND GYNAECOLOGY Table 1. Alternative perspectives in cost analysis. Perspective

Examples of resource items


Staff, equipment, ward space


Drugs, staff

General practice +

Community nursing, ambulance

Other health service

Health service +

Travel costs, additional child-minding, time costs incurred by patient

Patients + Patients' employers

Patients' time costs incurred by employer


Home help

Social services


resources used as part of an intervention or programme or as a result of a clinical condition (for example, the number of days in hospital, the type and grade of staff during a therapeutic procedure, and the dose and type of prophylactic antibiotics used prior to surgery). The second stage involves attaching monetary values to (valuing) these measures of resource use by multiplying the resource use counts by the unit costs of the resources. Sometimes these unit costs are market prices (e.g. the unit cost of consumables such as swabs used in theatre), often they are estimated using hospital accounts (e.g. the unit cost of a day on a ward), and occasionally they are inferred from the market price of related resources (shadow prices; e.g. the unit cost of an informal carer might be based on the hourly wage rate of a care assistant). Estimating the cost of particular treatments can, therefore, involve measuring and valuing a range of different resources. Figure 2 shows the £1200 ~. £1000 £800 E

£600 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~\\\\\\\NN.\~\\'~

Q.. Q. o ¢..

£400 . . . . . . .

£200 . . . . . . . . ~


............... ~ TCRE

...... AH

Figure 2. The types of health service cost included in the economic evaluation of transcervical resection of the endometrium (TCRE) and abdominal hysterectomy (AH). ~ , Pre-operative; [ ~ , ward; n , post-operative; k~--~, re-treatment; ~ , operation; [[]m], GP. Based on data from Sculpher at al (1993).



types of cost that were estimated in an economic evaluation of transcervical resection of the endometrium (TCRE) versus abdominal hysterectomy for menorrhagia undertaken from the perspective of the health service (Sculpher et al, 1993, 1996). In critically appraising a cost analysis, it is important to be confident that all relevant costs have been included, given the perspective of the study.

Measuring outcomes A range of outcome measures is now used to evaluate health-care technologies. These can broadly be divided into clinical measures and patient-based measures. Clinical measures are based on the physiological or pathological measurements of clinicians and may have little relationship to patients' feelings of well-being, for example objective measurement of menstrual blood loss as an outcome measure of drug treatment of menorrhagia. Patient-based outcome measures are more closely related to patients' perceptions of illness and, in terms of a spectrum of sophistication, run from measurement of patients' symptoms to a detailed assessment of the impact of a condition and its treatment on patients' functioning and general well-being (Patrick and Erickson, 1993). The latter is often described as a measure of health-related quality of life; for example, an instrument has been developed to assess the impact of menorrhagia on various aspects of a woman's life (Ruta et al, 1995). Of course, some measures of outcome are of great interest to clinicians and patients alike, mortality and survival rates being the most obvious examples. Many of the various outcome measures used in clinical evaluation have a central role in economic appraisal. However, outcome measures differ in their usefulness in economic analysis and in the informational value they offer for resource allocation. In judging the potential value of an outcome measure for economic evaluation, the following issues are relevant. I.


Is the measure uni- or multidimensional? Assessing the relative efficiency of health-care interventions is more straightforward when outcomes can be expressed on a unidimensional scale. For example, in a study looking at the cost-effectiveness of surfactant replacement in preterm babies, Mugford and Howard (1993) and Howard et al (1996) expressed outcomes in terms of the percentage of babies surviving treatment. When outcomes are inherently multidimensional, methods are required to express them on a single scale. Does the measure fully or partially reflect the relative effectiveness o f the interventions ? It makes little sense selecting an outcome measure for an economic evaluation because it is unidimensional if it only partially reflects the differences between interventions. For example, in the economic evaluation of TCRE versus abdominal hysterectomy referred to above (Sculpher et al, 1993), it would have been misleading to focus solely on days until return to work as the primary outcome measure and to ignore important effects such as pain and effect on menstrual symptoms. Cuckle et al (1995) assessed the cost-effective-



ness of antenatal screening for cystic fibrosis using affected pregnancies detected as their outcome measure, but this measure arguably neglects some important effects of the intervention, such as the anxiety felt by women and their partners in undertaking the test. . Is the measure specific to the condition or generic? Decisions about resource allocation often need to be taken across specialties and disease areas rather than within them. If these decisions are to be informed by economic analysis, it is crucial that the outcome measure adopted is generic, i.e. that it has meaning outside the clinical area within which it is used. For example, Marks et al (1990) assessed the costeffectiveness of a smoking cessation programme in pregnant women and expressed outcomes in terms of low birthweight infants avoided. However, this condition-specific outcome measure would be of little use to a decision-maker faced with the choice of allocating additional resources to that programme versus, for example, a new drug for hypertension, because there would be no basis of comparison between them.

Time horizon For many conditions, particularly chronic ones, the resource implications and effects on patients' health take place over many years. It is, therefore, important that an economic assessment has a time horizon that is consistent with the duration of these effects. For example, in Sculpher et al's (1993, 1996) analysis of the costs of alternative surgical treatments for menorrhagia, the mean cost per patient of TCRE 4 months after surgery was 53% that of abdominal hysterectomy; at 2.2 years follow-up, that proportion had increased to 71% as a proportion of women initially undergoing TCRE required additional surgery. In these circumstances, it would be inappropriate to reach definite conclusions about relative costs prior to adequate follow-up. The time dimension of costs and outcomes gives rise to another key concept in economic analysis, that of the process of discounting costs and benefits (Krahn and Gafni, 1993). Discounting involves adjusting future costs and benefits downwards to express them in terms of their present value. The rationale for discounting can be explained in two ways. The first of these is positive time preference, which suggests that as individuals and as a society we prefer good things (such as improvements in our health) earlier and bad things (such as resource costs) later, so that costs and outcomes that occur in the future are valued less than those that occur in the present. Discounting is an attempt to reflect this preference in the calculus of economic evaluation. Evidence actually indicates that individuals' intertemporal preferences are rather more complex than discounting assumes (Loewenstein and Prelac, 1993; Redelmeier et al, 1994; Dolan and Gudex, 1995). The second explanation of discounting is the concept of opportunity cost introduced earlier. The clearest illustration of this is that if £50 000 is used to buy a piece of equipment today, the interest that could have been earned had the money been placed in a bank deposit account is lost. Hence the cost



of a resource today can be considered greater than its cost in 10 years time because of the opportunity costs that are incurred over the 10-year period. The opportunity cost rationale is firmer when discounting relates to costs rather than to outcomes, and this is one reason why the discounting of outcomes at the same rate as costs in economic evaluation has been questioned (Parsonage and Neuberger, 1992).

Sources of evidence The quality of an economic evaluation will in part be determined by the quality of the data sources. The debate that exists in clinical evaluation about the value and feasibility of randomized controlled trials (RCTs), relative to observational studies (Black, 1996), also takes place in relation to economic analysis. Increasingly, RCTs are being used as a vehicle for the collection of resource use and outcome data for economic evaluation (Drummond, 1994). For example, the economic analysis of alternative treatments for menorrhagia (Sculpher et al, 1993, 1996) involved the collection of a range of outcome and resource use data on 196 women randomized to TCRE or abdominal hysterectomy (Dwyer et al, 1993). However, data from RCTs are often not available for all parameters within an economic evaluation, and a mix of experimental and observational data is required to assess the efficiency of interventions. For example, in their economic analysis of prophylactic antibiotics in caesarean section, Mugford et al (1989) used a meta-analysis of 58 RCTs involving 7777 women to generate their measure of outcome, which was risk of infection after caesarean section. To estimate the resource implications of postnatal care after a caesarean section, with and without a wound infection, the authors used a combination of hospital statistics and an observational study of a cohort of 486 women undergoing caesarean section. When an economic evaluation consists of data from a range of different sources, decision analysis is often used as a means of synthesizing the various parameters. Decision analysis is an intuitive framework for systematically structuring problems characterized by uncertainty (Weinstein and Fineberg, 1980; Thornton and Lilford, 1995; Dowie, 1996) and involves defining a range of possible clinical pathways, characterizing them in terms of costs and outcomes, attaching probabilities and calculating the probability-weighted costs and outcomes of alternative interventions. For example, Mohie-Boetani et al (1993) used a decision analytic model and data from various sources, including cohort studies and a surveillance system, to estimate the expected costs and outcomes of three prevention strategies for early-onset neonatal group B streptococcal disease. The authors found that the strategies could prevent between 46% and 58% of neonatal disease and would result in a net cost saving.

Dealing with uncertainty All economic evaluations produce estimates of the costs and outcomes of interventions in conditions of uncertainty. This will be associated with the



data inputs, such as estimates of resource use, the probability of particular clinical events and the unit cost of resources; the methods of analysis used, such as the discount rate employed; and the extent to which the analysis can be generalized to routine clinical practice (Briggs et al, 1994). In these circumstances, it is essential to assess how robust the findings of the study are to alternative parameter values and assumptions using sensitivity analysis. For example, in Sheldon and Simpson's (1991) economic analysis comparing prenatal screening for Down's syndrome using the 'triple test' with screening based on maternal age based on a risk cut-off for amniocentesis, the authors found that the triple test was more cost-effective as long as detection rates above 45% could be achieved.

DECISION RULES IN E C O N O M I C EVALUATION The starting point for any economic analysis is the estimation of the full range of costs and outcomes of the interventions under comparison. Some studies begin and end at this point, providing decision-makers with evidence about the costs and effects without trying to identify which is the more efficient. This sort of analysis is sometimes referred to a costconsequence analysis, and the onus is on the decision-maker to judge the implications of the results for efficiency. The economic analysis of TCRE versus abdominal hysterectomy by Sculpher et al (1993, 1996) is an example of this sort of analysis: the study detailed the various resource costs and outcomes, but reached no conclusion about the economically preferred option.

Comparing costs and outcomes For many studies, the description of costs and outcomes is just the starting point in the overall assessment of relative efficiency, which requires the formal comparison of costs and outcomes to judge whether any of the conditions detailed in Figure 1 above exists, based on some clear decision rules. This process is illustrated in Figure 3, which is often referred to as the cost-effectiveness plane (Black, 1990). To explain the diagram, assume that the costs and outcomes of two treatment options A and B are being compared. The vertical axis of the graph shows the differential cost of the two interventions (CostA-- CostB): A is more costly than B above the origin, and B is more costly below the origin. The horizontal axis relates to the differential outcomes achieved by the two options (Outcomen-- Outcom%): A is more effective to the right of the origin, and B is more effective to the left of the origin. On this basis, it is possible to define four quadrants. In quadrant I, A is more costly and more effective; in quadrant II, A is less costly and more effective; in quadrant III, A is less effective and less costly; and in quadrant IV, A is more costly and less effective.



Cost difference A S ## # ¢s

gV #s #s s# #s sS d




Outcome difference


Figure 3. The cost-effectiveness plane: a diagrammatic representation of the costs and outcomes of two interventions.

Establishing the presence of dominance The most straightforward decision rule concerning relative efficiency states that if an intervention, say A (using the example above), dominates B, it is clearly more efficient. That is, the comparison of the two options falls into quadrant II, and A is less costly and more effective. Similarly, if the comparison fell into quadrant IV, A would be more costly and less effective, and would be dominated by B. Under either of these conditions, the decision-maker would feel safe in concluding that the dominant option was the more efficient. An example of this sort of analysis is Mugford et al's (1989) evaluation of prophylactic antibiotics in caesarean section. On the basis of the metaanalysis of trials, the study found that the intervention reduced the odds of wound infection by 50-70%, and it was estimated that antibiotics would reduce the average cost of postnatal care by £ 1300-£3900 depending on the cost and efficacy of the antibiotic used. Hence prophylactic antibiotics were found to be the dominant option. Another example of the existence of dominance in an economic evaluation is a comparison of intramuscular, when required, pain relief and intravenous patient-controlled analgesia (PCA) after abdominal hysterectomy (GiUman and Robertson, 1995). Using a visual analogue scale, the authors found no statistical difference in pain scores between two groups randomly allocated to the two forms of pain relief. In addition, they found that intramuscular 'on-demand" analgesia had a mean cost of £12.64, while the mean cost of PCA was £42.68. Hence it was concluded that on-demand



pain relief was found to be the dominant option. This form of analysis, where outcomes are found to be equal and relative efficiency depends solely on differential cost, is sometimes referred to as cost-minimization analysis (Drummond et al, 1987).

Assessing relative efficiency when dominance is not established In practice, it is rare that the costs and outcomes in an economic study lend themselves to the dominance decision rule, and it is usually the case that one option is more effective than the other, but also more costly (quadrants I and III in Figure 3). The key question here is, 'Is the incremental cost worth paying for the additional benefits generated?', and decision rules developed to answer this question focus on the incremental cost per additional unit of outcome with the more effective option. In the case of the comparison of options A and B, the incremental cost per additional unit of outcome of A relative to B is calculated as (CostA- CostB)/(Outcomea- Outcom%), and is represented by the gradient of the dotted line in quadrant I. In the circumstances of non-dominance, the characteristics of outcome measures discussed above become all-important. The first important characteristic is that the outcome measure used in a study should be unidimensional in order that it is unequivocal that one intervention is more effective and that it is possible to express additional costs and effects in the form of an incremental ratio. The second important characteristic of an outcome measure when there is no dominance is the level of efficiency in which the decision-maker is interested. If the decision-makers are a team of clinicians concerned with maximizing the outcomes they achieve in the management of a specific clinical condition, the outcome measure they use should simply be relevant to that condition. If, on the other hand, the decision-maker is a purchaser responsible for maximizing the benefits from health care in a range of specialties and disease areas, the outcome measure required should be generic, i.e. it should have meaning across the whole health-care system. It is helpful to think of economic evaluation being used to inform decisionmaking at three levels of efficiency, and these are described below.

Level of efficiency: disease area If the decision-maker is concerned with maximizing the outcomes that can be achieved from a fixed budget in a specific clinical area, the requirements are that the outcome measure adopted in an economic appraisal should be relevant to the clinical area, an adequate representation of the differential effectiveness to the options under consideration, unidimensional and widely used in studies mounted to inform resource allocation in that area. There are a number of examples in the obstetrics and gynaecology field of studies seeking to inform decision-making at this level. In the area of prenatal screening, several studies have been undertaken which report the incremental cost of a programme per affected pregnancy detected (Sheldon and Simpson, 1991; Cuckle et al, 1995), on the basis that detecting these



pregnancies is the principal objective of these programmes. In their economic analysis of surfactant replacement in preterm babies, Mugford and Howard (1993) reported the incremental cost per additional surviving baby. In Howard et al's (1996) evaluation of ECMO, the incremental cost per additional survivor without severe disability was presented. Marks et al (1990) provided information on the costs and outcomes of a smoking cessation programme in pregnant women in terms of the incremental cost per low birthweight infant prevented. When a condition-specific measure of outcome is used in an incremental ratio, the study is usually referred to as a cost-effectiveness analysis (Drummond et al, 1987). In order to maximize the outcomes achieved in a given clinical area, the decision-maker would need to compare the ratios reported in all the relevant studies and allocate additional resources to those programmes with the lower incremental ratios, or fund these interventions by removing funding from those programmes with relatively high ratios (Johannesson, 1995). In practice, information of this type is not available on all interventions and programmes in a given disease area, but the presentation of incremental ratios provides a useful indication to decision-makers of the size of the additional benefits that can be achieved by providing extra resources or by adjusting the existing pattern of resource allocation.

Level of efficiency: health-care system Health-care purchasers are likely to take a broader perspective in terms of the outcomes they are trying to maximize. In principle, their focus is the efficiency at the level of the whole health-care system. This involves judgements about the relative value of outcomes achieved in clinical areas as diverse as obstetrics and gynaecology and dermatology, which means that condition-specific outcomes need to be translated into a generic measure of benefit that has meaning in all these areas. In recent years, there has been an attempt to develop such a genetic measure of benefit for economic evaluation in the form of the qualityadjusted life year (QALY). The premise of the QALY is that health care seeks to generate two general forms of outcome, increased life expectancy and improved health-related quality of life, and the measure embodies both elements. Figure 4 provides an illustration of how QALYs are calculated. For a given condition, length of life is measured along the horizontal axis in terms of life years. The vertical axis shows how each life year is weighted to represent the health-related quality of life typically associated with it using a scale running from 0 (equivalent to death) to 1 (equivalent to good health). The area under these 'QALY profiles' represents the expected QALYs associated with the prognosis following a specific intervention, and the difference between the areas under the two profiles represents the additional QALYs generated by one intervention over another (in this case the shaded area X minus the shaded area Y). In addition to being a generic measure of benefit in health care, the QALY can be used as a means of combining multidimensional outcomes of interventions such as pain and social function.




"6 t~



o "1-

Length of life Figure 4. QALY profiles for two interventions.

To estimate QALYs in any specific study, two data sources are required. Life expectancy is usually taken from available clinical studies, although these may only report mortality or survival rates requiring assumptions if these are to be translated into estimates of life expectancy (Beck et al, 1982). The more difficult data source to acquire to calculate QALYs is the weights (often referred to as utilities, preferences or values) to qualityadjust the life years. Various methods exist to measure these weights, which, in terms of sophistication, range from the use of plausible ad hoc values posited by the authors of studies, to the use of choice-based instruments to elicit from individuals the weights they would attach to particular states of health (Patrick and Erickson, 1993). Figure 5 illustrates how one of these choice-based instruments, the time trade-off, is typically used to elicit weights for chronic states of health (Torrance, 1986). An individual is presented with a description of the health-related quality of life generally associated with the state of health for which a weight is required (Hi). He or she is asked to imagine being in this state of health for their estimated life expectancy (T years, alternative 2), and then asked to compare that prospect with being in good health for a period of time shorter than their life expectancy (attemative 1). Using an iterative procedure, the period of years in good health such that alternatives 1 and 2 are considered equivalent by the individual (X years) is elicited, and the weighting Hi is calculated as X/T. Alternative versions of the time trade-off are available to elicit weights for states of health that are temporary rather than chronic and for those considered worse than death (Torrance, 1986). In estimating weights for states of health, a decision needs to be taken regarding from whom these should be elicited. One approach is to use the time trade-off directly on patients in a prospective study to value their state of health over time. The more usual approach is for weights to be elicited



A#ernativQ t


Alternative 2

0 0


T time

Figure 5. The time trade-off instrument as used to elicit quality weights to calculate QALYs.

from a group of patients, clinicians or members of the public based on descriptive scenarios. In recent years, the effort of having to elicit a set of weights for each and every QALY-based economic evaluation has been avoided by the development of standardized generic classifications of states of health that, in principle, can be applied to all clinical areas, providing a predetermined weight for each state of health. An example of this sort of system is the EuroQol, which defines states of health in terms of five dimensions: mobility, self-care, usual activities, pain and emotion, each dimension having three levels that can be used to classify patients into one of 245 states of health (Brooks, 1996). On the basis of interviews undertaken with 3395 randomly selected members of the public using the time trade-off, weights have been estimated for each of these states of health (Williams, 1995). Therefore, if prognoses can be described in terms of these various EuroQol states of health, QALY profiles can be defined for various interventions without specially having to elicit weights. Economic evaluations using QALYs also relate costs and outcomes in the form of a ratio: the incremental cost per additional QALY. Although this form of analysis is essentially a special case of cost-effectiveness analysis, it is usually referred to as cost-utility analysis (Drummond et al, 1987). For example, Daly et al (1992, 1993) interviewed 63 women recruited opportunistically at a general practitioner's clinic to elicit, using the time trade-off instrument, the values they attached to states of health characterized by different levels of severity of menopausal symptoms. These values were then incorporated as weights for the calculation of QALYs into an economic evaluation of hormone replacement therapy (HRT). The incremental cost per additional QALY of HRT was estimated at between £700 and £6200, depending on the severity of symptoms experienced, the type of HRT and whether the woman had a uterus. As increasing numbers of cost-utility analyses are undertaken in a range of different clinical areas, purchasers will have more information on which to make decisions about resource allocation across specialties and disease areas. This will allow them to adjust funding away from areas with high incremental costs per additional QALY towards those programmes and interventions with lower ratios. As a measure of benefit,



the QALY has the disadvantage that it is based on some strong assumptions (Loomes and McKenzie, 1989). For example, it is assumed that individuals would be indifferent between spending 2 years in a state of health valued at 0.5 followed by 2 years at 0.75, and a year at 0.5 followed by 2 years in good health. Alternative generic measures of benefit, requiring fewer assumptions of this type but based on principles similar to those of the QALY, are being developed (Mehrez and Gafni, 1989), but their greater sophistication comes at the cost of increased complexity in terms of measurement. The major advantage of QALYs is that they are an explicit means of trading off the various consequences of health-care interventions using a standard generic scale. While they should not be interpreted blindly by decision-makers, they provide a useful managerial tool for purposes of resource allocation across the healthcare system.

Level of efficiency: society Cost-effectiveness and cost-utility analyses focus on the additional outcomes/benefits generated by incremental resources, and neither answers the question of whether these interventions should be provided at all. However, this is the sort of question that decision-makers responsible for allocating resources at the level of society in general, to health versus transport versus education, have to ask: are the outcomes generated by these health-care interventions greater than the benefits that are thus forgone (opportunity costs) elsewhere in the economy. Economic evaluation designed to answer this question is termed cost-benefit analysis and is faced with the task of valuing both the resource implications and the outcomes of interventions in monetary terms and expressing the efficiency of an intervention in terms of its net cost or benefit. Although published studies frequently purport to be cost-benefit analyses, genuine examples of the use of this type of analysis are rare in health care. The main reason for the underuse in health care of the most powerful form of economic evaluation is the difficulty in expressing the effects of interventions on health and other sources of patients' well-being in monetary terms. Recently, however, a method has been more widely applied to health to achieve this monetary valuation. Described as willingness to pay, this technique asks groups of patients or other individuals for their hypothetical willingness to pay for the outcomes of interventions (Donaldson, 1990; Gafni, 1991; O'Brien and Viramontes, 1994). An advantage of willingness to pay is that it can elicit values for aspects of interventions that are not outcomes in the usual health-related sense of the term, but are more closely related to the process of care (Ryan and Shackley, 1995). For example, Berwick and Weinstein (1985) used willingness to pay to elicit the value that women attached to the information generated by an ultrasound during pregnancy that did not have any significance in terms of clinical decision-making. The study found that, among the women interviewed, 26% of the value they attached to a scan relate~ to information with no clinical significance.



SUMMARY It is an economic fact of life in all health-care systems that decisions will have to be taken about which programmes and interventions should and should not be provided. The splitting of the roles of purchasing and providing within the UK's NHS has made these decisions more explicit and, together with devolved budgets, required more health service professionals to understand the analytical methods of economic evaluation. Certainly, increasing numbers of economic studies will be undertaken in obstetrics and gynaecology. Checklists exist to assist decision-makers in judging the quality of published studies (Drummond et al, 1987), but it should be emphasized that different studies seek to answer different questions. In particular, it is important to be aware of the level of efficiency being addressed in an analysis.

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