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Health Policy,8 (1987) 317-323 Elsevier HPE 00179
A Second Opinion
Cost of illness studies: making? Alan Shielll,
an aid to decision-
Karen Gerard’ and Cam Donaldson2
‘Centre for Health Economics, University of York, U.K. and 2Health Care Research Unit, University of Newcastle upon Tyne, U.K. Accepted
5 September
1987
Summary Health Policy, 6 (19861 119-143 contained an article by Henke and Behrens which purported to estimate the economic costs of illness in the Federal Republic of Germany. The paper is one of the latest in a collection of empirical studies which are linked by a common approach and methodology dating from Rice’s seminal work in 1966. It has been argued that such work is of value in indicating the burden of disease and in setting priorities in research, prevention and treatment. Indeed it has been claimed that cost of illness studies are an essential component of the evaluation of alternative demands on scarce health care resources. However, cost of illness estimates are based on unsound theory which leads to circularity and bias in their policy prescriptions. The shortcomings of the technique are both well known and well documented yet cost of illness estimates continue to be produced. In response to this we would like to restate the theoretical errors of the approach and indicate its limitations for policy. We would also like to remind policy-makers that economics can offer something more useful than a method for estimating the ‘benefits of the unattainable’. Cost-benefit analysis provides a framework in which information relevant to the decision at hand, on the effectiveness and resource consequences of policy options, can be considered in a systematic manner. Health economics;
Cost of illness studies; Human capital theory
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Introduction The problem of scarce resources is common to all health care systems whatever their organisational structure. Health service managers and policy-makers have to make choices between policy options in an attempt to reconcile this economic fact with competing demands for care. As a general approach to the problem of resource allocation, economists suggest the objective of economic efficiency. This implies that no programme should be adopted unless its benefits exceed its costs and, where two programmes compete for resources, priority should be given to the programme with the greatest net benefit (i.e. benefit minus cost) [l]. Although concern for the distribution of costs and benefits might temper this general approach, it is clear that health planners and managers require pertinent and relevant information on the likely costs and benefits of policy options if they are to make the most of scarce resources. As a result there is now a substantial health economics and health policy literature addressing the issue of economic evaluation in health care [2-4]. A significant feature of this ‘literature comprises cost of illness (COI) studies. For example, one section of a recent health-economics bibliography comprises papers of this sort [5], and it has been suggested that more than 200 such studies have been reported since 1960 [6]. The most recent example is a study estimating the alleged costs of illness in the Federal Republic of Germany, published in this journal only last year [7]. The abundance of CO1 studies is said to be indicative of their value in promoting rational decision-making, particularly the contribution they offer to public policy formulation and priority setting in health care [8]. For instance, it has been claimed that CO1 estimates are recognised to be ‘. . .an essential component of the evaluation of alternative demands on our scarce health care resources. . . ’ although it is admitted that one of the most common uses of the technique has been merely to satisfy curiosity about the aggregate burden of disease [9]. More explicitly, it has been suggested that ‘... to allocate scarce resources efficiently, estimates of the economic costs associated with specific illnesses and injuries must be calculated; these estimates become the economic benefits associated with the alternative resource allocations [lo] (our emphasis). The aim of this article is to examine the alleged relationship between the socalled economic costs of illness and the benefits of policy options designed to prevent, control and treat the disease in question. Particularly, it is the strength of this relationship, its theoretical basis and the relevance of CO1 studies to policy issues which are critically explored in this article. In the following sections consideration is given to the standard methodological framework used in CO1 studies and 5 criticisms of the technique. It is concluded that although more information is certainly needed on the social impact of illness, the natural history of disease and the effectiveness of different medical interventions, CO1 studies will only confuse, mask and mislead decision-makers.
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Background The seminal work in the CO1 field was Dorothy P. Rice’s 1966 study which purported to estimate the costs of illness by ICD category [ll]. Previous authors had set out a framework for costing individual diseases [12,13], and by extending this to all disease categories, Rice hoped to establish a standard methodology. Judged by the widespread application of the technique, in several countries, Rice was certainly successful in meeting her objective [7,14-161 whilst Rice and her colleagues continue to replicate and update the original estimates [17,18]. In each of the studies referenced above the cost of illness is defined to include both direct and indirect elements. Direct costs arise because resources are used to prevent, detect and treat disease and cost estimates are obtained by summing the expenditures on each category attributable to the disease in question. Indirect costs relate to the loss of productive output caused by absenteeism, early retirement or premature mortality. This is valued using the ‘human capital’ approach which regards the value of a human life as equivalent to its discounted expected future income. Average earnings data standardised for age and sex can then be used to value the lost output. Both these elements are readily quantifiable and are often termed the ‘economic’ cost to distinguish them from the psychosocial effects of illness. Welfare effects such as disability and distress tend to be excluded from the calculus because of their intangible nature although some CO1 analysts do allude to their existence. Therefore, at best, the technique provides only a partial estimate of the detrimental effects of illness.
Criticisms of COI studies Measurement of the benefits of health-care programmes has been a major obstacle preventing more widespread use of economic techniques in health service management. Therefore, the claim that cost of illness estimates become the economic benefits of treatments is a powerful one. However, although there appears to be an intuitive economic logic to the reasoning which equates the so-called costs of illness to the benefits of treatment, it rests on a fundamental misunderstanding of the concept of economic cost. Building upon this misunderstanding CO1 analysts proceed to ignore the importance of any policy context and to rely on the outdated human capital approach to valuing economic costs and benefits. This in turn leads to circularity in CO1 estimates and, therefore, in priority setting. The concept of economic
cost
In economic terms costs arise because resources are scarce and have alternative uses. The decision to commit resources to one activity therefore denies society the opportunity of enjoying the benefits of these other uses. The cost of the chosen option is represented by the value of the forgone benefits of the most favoured
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alternative use. Hence economic costs arise from decisions about activities affecting resource use. Illness is neither an activity nor, except in the case of the consumption of hazardous goods, is it the result of a conscious decision. Therefore, costs do not arise from illness per se but from decisions to commit resources to the treatment of disease. There is a symmetry between economic costs and forgone benefits but this should not lead one to confuse the two and mistakenly ascribe costs to illness or claim that these ‘costs’ are the benefits of alternative treatment options. The objective of health care policy is to maximise a combination of the quantity of life and its quality, measured ideally in terms of quality-adjusted life years (QALYS). The benefits of alternative treatment options, therefore, are reflected in improved health status and not the potential averted costs of disease. Policy context
Notwithstanding the above criticisms the total ‘costs of illness’ can only indicate the benefits of treatment options if an intervention is capable of totally eradicating or entirely preventing the disease in question. This is only likely to be possible in the case of a very few infectious diseases. The most pertinent questions facing policy-makers usually relate to scale; that is, by how much should an existing programme be expanded or contracted. The answer to this question requires a marginal analysis which compares the expected change in benefits with the costs of the intervention which brings that change about. CO1 studies fail on this point by totally ignoring the importance of the margin. Costs will be incurred by different treatment options which will achieve improvements in health status in differing amounts. The problem for the policy maker is to identify the programmes which maximise the change in health state given the resources available. This problem is best approached by examining current uses of resources, identifying possible alternative uses and enumerating the costs and benefits of each. Looking at marginal changes in the budget, such as the introduction of a new technology, alternative uses of resources should be compared in terms of the gain in quality-adjusted life years and net costs (costs of the new project minus costs saved). When examining possible uses of an overall (or newly created) budget, alternatives need simply to be compared in terms of the costs of the projects and the gains in quality-adjusted life years. Although good epidemiological data on the total consequences of disease may be necessary to aid the identification of possible priority areas, it seems irrelevant to go one step further and place monetary values on these total consequences when policy changes at the margin affect only a small proportion of the consequences. It is, therefore, more detailed marginal analysis which will aid the ultimate identification of priorities. Human capital theory
A more significant criticism of the CO1 methodology arises from its use of human capital theory. This technique’s reliance on earnings data to value productive
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capability leads to unavoidable bias towards those diseases which affect white, middle-class males in employment. In an estimate of the costs of 4 major health impairments Hartunian et al. [19] candidly confess that the ‘...discrepancy between true societal values and expected future incomes is evident: our society simply does not agree that the average male between the ages of 25 and 34 is 1.6 times as valuable (as the earnings data indicate) as the average female of comparable age.’ Given that it is not an explicit priority of society to return its waged members to full health before its unwaged members, it may be more relevant to report such costs in terms of loss of life years (better still quality-adjusted life years). It is also not at all apparent, as Hartunian and his colleagues assert, that the human capital technique can be used in a more limited way to estimate the ‘economic impact of disease’. Although it may seem reasonable to assume some loss of productive output as a result of illness, no actual loss of production is demonstrated and it can be questioned whether any actually occurs [20]. Most firms will make allowances for a given level of sickness-absenteeism in their production and employment plans and with current levels of unemployment those in the workforce who are unfortunate enough to die or need to retire early can be replaced at little cost. As far as society is concerned, there is little or no opportunity cost in terms of lost production. Furthermore, even if production is lost it is dubious whether earnings data can be used to value it. This practice depends on the assumption of a perfect labour market which is not a characteristic of the real world. The circularity
of COI estimates
Mistakenly identifying the costs of one treatment as part of the potential benefits of another leads to a circularity in the thinking of CO1 analysts which threatens to bias their policy prescriptions. As Drummond et al. [5] note, this practice may lead to priority being given to those health programmes which are apparently costly because they already have a large amount of resources devoted to them. If past resource allocation decisions have been made in an irrational manner, as is suggested by the need for economic appraisal, then subsequent policy decisions perpetuate and amplify the initial mistake. Stimulus
for new research
A final attempt to justify the production of CO1 estimates is made by the authors of the West German study recently published in this journal. Henke and Behrens [7] acknowledge the technical limitations of their analysis but quote the renowned economist Kenneth Arrow in support of their claim that the production of poor data is better than no data at all because of the stimulus provided to new research. This is an ingenious argument which on the evidence of their own paper simply does not hold. The method adopted by Henke and Behrens is the same as that used by Rice 20 years previously and includes the same faults and theoretical shortcomings. Rather than encourage new techniques, research effort (which is itself a scarce resource) has been wasted replicating the same mistakes. The infor-
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mation generated is, at best, useless and, at worst, biased. With no encouragement to new research and the waste of scarce resources on a useless activity in what way can it be better than no data at all?
Conclusions The economic evaluation of health care has certainly suffered from a lack of data. More information is needed on the social impact of illness, the natural history of disease and the effectiveness of different medical interventions [21]. Our concern is whether the total treatment implications, production losses and welfare consequences of illness need to be valued in monetary terms as such values, obtained as they usually are from imperfect markets, do not (or rather should not) enable choices to be made about the allocation of society’s scarce resources. While there is undoubtedly a demand for good community physicians, the economic epidemiologist has no role to play in the decision-making process. The real dangers of CO1 estimates are that they may confirm the prejudices of other social scientists about the alleged preoccupation of economists with money and may lead policy-makers to believe that they are all that economic analysis can offer [5,22]. Feldstein [22] stated that CO1 studies represented a method for ‘...calculating the benefits of unattainable goals.’ The resulting estimates were biased and lacking in any policy relevance. Unfortunately these criticisms remain as valid today - 200 studies and more than 20 years later! Health economics does have more to offer health service planners. It can provide a systematic framework in which to assess the marginal costs and benefits of alternative policy options. The ranking of treatment programmes in terms of their cost/QALY scores will identify opportunities for the beneficial reallocation of resources and the setting of priorities. Contrary to this CO1 studies only confuse, mask and mislead.
Acknowledgements The authors would like to thank their fellow members of the Health Economists’ Study Group, particularly Professor M. Drummond, Dr. S. Birch and Dr. A. Wagstaff, for their useful comments on an earlier draft of this paper.
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323 5 Drummond, M.F., Ludbrook, A., Lowson, K. and Steele, A., Studies in Economic Appraisal in Health Care, Vol. 2, Oxford University Press, Oxford, 1986. 6 Hu, T. and Sandifer, F.H., Synthesis of Cost of Illness Methodology. Prepared under Contract No. 233-79-3010 for the National Centre for Health Services Research, Washington, DC, Public Services Laboratory, Georgetown University. 1981. 7 Henke, K.D. and Behrens. C.S., The economic cost of illness in the Federal Republic of Germany in the year 1980, Health Policy, 6 (1986) 119-143. 8 Hodgson. T.A., The state of the art of cost of illness estimates, Advances in Health Economics and Health Services Research, 4 (1983) 129-164. 9 Hodgson. T.A. and Meiners, M.R., Cost-of-Illness Methodology: A Guide to Current Practices and Procedures. Millbank Memorial Fund Quarterly/Health and Society. 60 (3) (1982) 429-462. 10 Tolpin, H.G. and Bentkover, J.D., Economic costs of illness. Decision-making applications and practical considerations, Advances in Health Economics and Health Services Research, 4 (1983) 165-198. 11 Rice, D.P., Estimating the Cost of Illness, Washington. U.S. Government Printing Office, PHS Publ. No. 947-6, Health Economics Series, 1966. 12 Fein, R., Economics of Mental Illness. Basic Books, New York, 1958. 13 Weisbrod, B.A., The Economics of Public Health, University of Pennsylvania Press, 1961. 14 Holtman, A., The size and distribution of benefits from U.S. medical research: the case of eliminating cancer and heart disease, Public Finance, 28 (1973) 354-361. 15 Lindgren. B., Costs of Illness in Sweden 1964-75 I.H.E. Lund, 1981. 16 Andrews, G., Hall, W., Goldstein, G., Lapsley, H., Bartels. R. and Silove, D., The economic cost of schizophrenia: implications for public policy, Archives of General Psychiatry, 42 (1985) 537-543. 17 Cooper, B.S. and Rice, D.P., The economic cost of illness revisited, Social Security Bulletin, 39 (2) (1976) 21-36. 18 Rice, D.P., Hodgson, T.A. and Kopsten, A.N., (1976) The economic costs of illness: a replication and update. Health Care Financing. 7 (1985) 61-80. 19 Hartunian, N.S., Smart, C.N. and Thompson, M.S., The Incidence and Economic Costs of Major Health Impairments, Lexington Books, 1981. 20 Moore, R.H. and Buschbom, R.L., Work absenteeism in diabetics, Diabetics, 23 (1974) 957-961. 21 Cochrane, A., Effectiveness and Efficiency - Random Reflections on Health Services, Nuffield Hospitals Provincial Trust, London, 1972. 22 Feldstein, M.S., Review of B.A. Weisbrod’s ‘The economics of public health; measuring the economic impact of diseases’, Economic Journal, 73 (1963) 129-130.