Health Care Analysis 1999,7,167-175

 Erik Nord, National Institute of Public Health, P.O. Box 4404 Torshov, N-0403 Oslo, Norway.

phone: (47) 22 04 23 42, fax: (47) 22 04 25 95, e-mail:

 Key words: health economics, QALY, equity, fairness, cost-value analysis, SAVE


By describing societal value judgements in health care in numerical terms one may in theory increase the precision of guidelines for priority setting and allow decision makers to judge more accurately the degree to which different health care programs provide societal value for money. However, valuing health programs in terms of QALYs disregards salient societal concerns for fairness in resource allocation. A different kind of numerical valuation of medical interventions, that incorporates concerns for fairness, is described. The usefulness to decision makers of such numerical information remains to be tested (see also paper on transforming utilities (click).



 When we spend money, we want to get good value for it. This is true when we do private shopping . It is equally true when we are asked as citizens how we think scarce resources in a national health service should be distributed across patient groups.

Judging value in public health care is difficult, and better ways of doing so are being explored continuously. The purpose of this article is to present a recent development in numerical modelling of societal valuations of medical interventions, in which salient concerns for fairness across patient groups are taken into account in addition to concerns for efficiency. Such modelling opens up for a more comprehensive and valid type of economic evaluation – called cost-value analysis - than one has seen hitherto in health economics. Hopefully this development will increase the relevance of economic analysis in informing resource allocation decisions in health care.


Guidelines for priority setting

In national health services, some elected or selected individuals or groups of individuals are given the rather ungrateful task of judging the value of different health care programs relative to their costs and of prioritising between the programs in accordance with these judgements in budget decisions. To make their task easier, and ultimately to improve the performance of these analysts and decision makers, many countries have developed guidelines for priority setting in health care. The guidelines are based partly on ethical reflection in academics and policy makers (e.g. Daniels, 1985; Menzel, 1990; The Norwegian Commission on Priorities in Health Care, 1987), partly on measurements of values, attitudes and preferences in samples of the general population (e.g. Charny et al, 1989; The Oregon Health Services Commission, 1990; Campbell and Gillett, 1993; Olsen, 1994; Nord, Richardson et al, 1995; Ubel et al, 1996; Pinto Prades, 1997; Dolan and Cookson, 1998).

A review of existing materials of the above kinds in industrialised countries like Australia, England, Holland, New Zealand, Norway, Spain, Sweden and the US (Nord, 1996) suggests that ethicists’ and policy makers’ reflections, and results from public preference measurements, converge on the following points:

A. Society demands that medical interventions satisfy a minimum

requirement of effectiveness in terms of value to the patients concerned.

B. Society’s appreciation (valuation) of medical interventions increases strongly with increasing severity of the patient’s condition.

C. Life saving or life extending procedures are particularly highly valued, and significantly more highly than interventions even for patients with severe chronic conditions.

D. When the minimum requirement of effectiveness is satisfied (point A above), society worries less about differences in the size of the health benefits provided by treatment programs for different patient groups, the underlying attitude being that people are entitled to realising their potential for health, whether that be large or moderate given the state of art in different areas of medicine.

E. As a special case of point D, society in most cases does not wish to discriminate between people with different potentials for health in decisions about life saving or life extension. For instance, society regards the prevention of premature death in people with chronic disease as equally worthy of funding as the prevention of premature death in otherwise healthy people. (Life extending interventions for people in vegetative states or states of very low subjectively perceived quality of life is a different matter.)

Points A-E constitute a set of guidelines for resource allocation in a national health service. Presumably decisions makers are able to do a better job when such guidelines are available than when they are completely in the dark regarding the criteria that society at large would like to see applied in resource allocation decisions.

Verbal guidelines are, however, imprecise. Consider for instance two states of illness X and Y, where X is more severe than Y. Guideline B says that a treatment for X is to be regarded as more valuable than a treatment for Y (assuming equal effectiveness). But it does not say how much more valuable X is. Is it for instance so much more valuable that it would justify that a program for illness X were given priority over a program for illness Y even if, for the same amount of money, only half as many people could be treated in program X compared to program Y?

Similarly, guideline C gives decision makers only a very rough idea about the trade-offs that society would like to make between life saving and life improving programs. Guideline A says little about what is a minimum requirement of effectiveness, and, particularly given this unclarity, guideline D does not make it clear how little differences in effectiveness should count.


Cost-utility analysis

In theory it is possible to supplement verbal guidelines with numbers that may indicate more precisely to decision makers the trade-offs that society at large would want to make between health care programs that affect (a) different numbers of people at (b) different levels of severity of illness and with (c) different potentials for improvement in health. To provide such numbers as a potential aid to decision making has been the ambition of health economists for almost three decades (Culyer et al, 1971).

Until recently the numbers provided by health economists were based on the assumption that it is rational for a society to aim at maximising the sum of individual health benefits produced by its health care system within given budget constraints. Health benefits are calculated as a product of health related gains in quality of life and the number of years that patients get to enjoy health benefits. Health related quality of life has been measured in terms of ’utility’ on a scale from zero (dead) to unity (healthy), and the product of quality of life gains and years of benefit has been expressed in terms of gained Quality Adjusted Life Years (QALYs), see for instance Torrance (1986). The recommendation of health economists has been for decision makers to allocate scarce resources in health care such as to maximise the number of QALYs gained. This would be achieved by giving priority to medical interventions that have a relatively low cost per QALY gained, or in other words a favourable ’cost-utility ratio’.

It is increasingly clear that the basic premiss of this recommendation - that the goal of a national health service is to maximise health benefits - is incorrect. There is strong evidence that the societal structure of concern in health care is more like in points A-E above, in which the idea of health benefit maximisation has no place. A numerical supplement to verbal guidelines in health care priority setting must therefore be quite different from the numbers that hitherto have been on offer in health economics.


Cost-value analysis

A different set of numbers, that purports to be consistent with points A-E above, was suggested recently by a team of researchers from Australia, Norway, Spain and the US (Nord et al, 1999), see table 1. The table indicates the relative value assigned by society to different improvements in health on a scale where the improvement from ’dying’ to ’full health’ is assigned the value of 1. The severity scale in the table is a modified version of a scale constructed by Sintonen (1981). The health state descriptions at each level were chosen with a view to making each step up on the scale appear equally significant in terms of individual utility. With a few exceptions, subjects involved in a pilot study said that they perceived the intervals as quite equal in this sense (Nord, 1993). The states described at each level were also mapped into two different multi attribute health state scaling instruments, one based on magnitude estimation and category rating, the other on standard gamble and time trade-off. Both mappings supported the impression that the 7-point severity scale has fairly equal intervals in terms of individual utility (see Nord, 1993, for details).

The numbers in the table are based on a series of empirical studies in later years of the trade-offs that the general public would want to make between health care programs that affect (a) different numbers of people at (b) different levels of severity of illness and with (c) different potentials for improvement in health. I emphasise that the method of synthesis was informal, the ambition being only to indicate somewhat roughly what seems to be a widespread societal structure of concern.

The concerns for severity and life saving expressed in points B and C in the verbal guidelines above come through in the upper diagonal in the table: One step up on the scale is valued more highly (and much more so) the lower the start point. The concerns for effectiveness and realisation of potential expressed in points A and D come through in each horisontal line: A movement from any given start point scores better the higher the end point, but marginal value decreases significantly with increasing treatment effect. For instance, a person with a potential to go from level 7 to level 4 will score almost as much as a person with a potential to go all the way from level 7 to level 1. The concern for non-discrimination in matters of life saving or life extension is expressed in the bottom line of the table , according to which the avoidance of death scores 1 no matter what the resulting state is, although the bottom left hand cell is left void to indicate that at some level of severity the value of life extension will be questioned.

The first seven lines plus the lower right hand cell of table 1 correspond to a set of health state values as shown in table 2. If we accept that the eight-point scale approximates an equal-interval one in terms of individual utility, the table shows decreasing marginal societal value of utility gains. The numbers correspond to a curve for societal health state values as a function of individual utilities that is convex to the y-axis.

Table 2 is not applicable to interventions that extend life at levels below full health, as these in the main should be assigned full value, see above.

The numbers indicated above could in principle be multiplied by the number of years that people get to enjoy health improvements, as is done in QALY calculations. However, this is not recommendable. Multiplying with life years presupposes that societal value is proportional to the size of the health benefit. We know that it isn’t, cfr. point D in the guidelines above. For instance, Dolan and Cookson (1998) report that subjects would not discriminate between two programs that – all else equal - gave two different groups of patients 8 and 20 years of benefits respectively. The exact relationship between duration of benefits and societal value is not yet known. At the moment, use of numbers of the kind indicated above should therefore be restricted to comparisons of programs that are fairly equal in terms of duration of benefits, or comparisons of programs in which benefits are so durable that differences in duration between the programs become a minor concern.

Tables 1 and 2 refer to health problems in terms of reduced mobility. This is because so much of the existing societal preference data pertain to this particular dimension. To apply the numbers to other kinds of health problems, one needs to know where they belong on the severity scale of table 1. This may be judged by judging the effect on quality of life of those other problems compared to the effects on quality of life of the various mobility problems indicated in the table. Studies of quality of life in patients with disabilities and chronic illnesses, combined with the experience and expertise of health professionals, may facilitate such judgements.

Note finally that the approach described above purports to encapsulate concerns for several different aspects of response to health care - in this case initial severity, potential for health and the actual health gain - in one single set of numbers. Some may prefer to make the nature and the extent of the severity-effectiveness trade-off explicit by adopting a decomposed approach, in which separate equity weights are introduced for distributive concerns, see for instance Williams (1988;1997) and Dolan (1998). Interested readers are referred to Nord et al (1999) for a further exploration into this alternative. While technically different, the objective is the same: To obtain estimates of value that incorporate concerns for fairness and thus allow a more comprehensive and valid cost-value analysis of health care.


An example of cost-value analysis is a follows: Assume that intervention A takes one type of patient from level 6 to level 4 at a cost of 10,000 USD, while intervention B takes another type of patient from level 4 to level 1 at a cost of 5,000 USD. In terms of individual utility gain, B is more valuable (more efficient) than A (given that the steps on the severity scale are roughly equidistant in terms of individual utility). But the societal value of intervention A is 0.27, compared to 0.08 for intervention B. The societal value per 10,000 USD spent is 0.27 and 0.16 for A and B respectively, indicating that allocating resources to area A rather than B gives more value for money when not only concerns for efficiency, but also concerns for equity are taken into account. This may be useful information to decision makers.


Rationale and validity

Generally speaking, the rationale for describing societal value judgements in numerical terms is to increase the precision of guidelines for priority setting and to allow decision makers to judge more accurately the degree to which different health care programs provide societal value for money.

The kind of numbers indicated in this paper purport to do this job better than the utilities of conventional health economics. The reason is simply that societal value judgements are governed quite strongly by concerns for equity (in addition to concerns for the sum of individual utility gains), and that the most salient equity concerns have turned out to be amenable to quantification at the same level of measurement as individual utilities are.

However, even if societal value numbers conceptually are more relevant than individual utility scores in informing resource allocation decisions, the appropriate way of establishing such numbers remains to be determined.

One common criticism of societal value numbers is that people’s responses to numerical preference questions in mailed questionnaires are unreflective and unreliable. This is true. But there is nothing that prevents researchers from collecting preference data in more high quality ways, for instance in focus groups that discuss ethical issues carefully before each participant gives his or her responses to specific quantitative questions. This is already happening (Nord, 1995; Murray and Lopez, 1996; Dolan, 1998), and may very well become the default approach in the future.

Another issue is: From whom should societal preferences for resource allocation be elicited? Should it for instance be from the general public, or perhaps from a selection of highly reflective members of society?

This is really a political question, to which there is no simple value-free answer. In principle, analysts and health planners might be interested in having access to value tables representing a number of societal subgroups. The important thing to bear in mind is that any value table primarily expresses the values ot the specific group of people whose preferences where elicited in the first place. Potential users of the table will have to decide for themselves what status they wish to assign to the thoughts and values of that particular group.

Having said this, there is much to be said for the following approach: Judgements as to where on a scale of severity (as in table 1) different kinds of health problems belong are judgements of health related quality of life. In such judgements, people with personal experience with the problems in question can claim expertise, and they are therefore arguably the right people to ask. On the other hand, judgements regarding the relative societal values of different improvements on the severity scale are judgements of distributive fairness. In such judgements, it is not so clear that some are so much greater experts than others that judgements should be delegated to them. On this issue, the general public seems a more appropriate source of values (Nord et al, 1999).


Concluding remarks

In this paper I have claimed that there is a theoretical rationale for modelling society’s valuation of medical interventions in numerical terms, and that this modelling can be done in a meaningful and reasonably valid way.

It does not follow that such modelling in practice really will be felt as helpful by analysts, health planners and policy makers. Numerical approaches using indexes of value are inherently reductionist. They are also linguistically alienating to many people (not everybody likes numbers).

It would not be fair to judge the usefulness of numerical approaches on the basis of experiences with the specific numerical models that have been on offer hitherto in health economics, since these have been clearly off target due to their focus on health benefit maximisation. It is a task for empirical research to study whether an improved model as described in this paper will prove more helpful.






























Campbell A, Gillett G. Justice and the right to health care. In Ethical Issues in Defining Core Services. Wellington: The National Advisory Committee on Core Health and Disability Support Services, 1993.

Charny MC, Lewis PA, Farrow SC. Choosing who shall not be treated in the NHS. Social Science & Medicine, 1989;28:1331-8.

Culyer AJ, Lavers RJ, Williams A. Social indicators: Health. Social Trends 1971,2,31-42.

Daniels N. Just Health Care. Cambridge, MA: Harvard University Press, 1985.

Dolan P. The measurement of individual utility and social welfare. Journal of Health Economics 1998, 17, 39-52.

Dolan P, Cookson R. Measuring preferences over the distribution of health benefits. Mimeo. University of York: Centre for Health Economics. 1998.

Menzel P. Strong Medicine. New York: Oxford University Press 1990.

Murray C, Lopez A. The Global Burden of Disease. Harvard University Press 1996.

Nord E. The trade-off between severity of illness and treatment effect in cost-value analysis of health care. Health Policy,1993,24,227-238.

Nord E. The person trade-off approach to valuing health care programs. Medical Decision Making, 1995,15,201-208.

Nord E. Health status index models for use in resource allocation decisions. A critical review in the light of observed preferences for social choice. International Journal of Technology Assessment in Health Care 1996, 12,31-44.

Nord E, Richardson J, Street A, Singer P, Kuhse H. Maximising health benefits versus egalitarianism: An Australian survey of health issues. Social Science & Medicine,1995,41,1429-1437.

Nord E, Pinto JL, Richardson J, Menzel P, Ubel P. Incorporating societal concerns for fairness in numerical valuations of health programs. Health Economics 1999,8,25-39.

Norwegian Commission for Prioritising in Health Care. Retningslinjer for prioritering innen helsevesenet. (Guidelines for prioritising in health care.) NOU 1987:23. Oslo: Universitetsforlaget, 1987.

Olsen JA. Persons vs years: Two ways of eliciting implicit weights. Health Economics 1994,3,39-46.

Oregon Health Services Commission. Prioritization of health services. A report to the Governor and Legislature. Salem, Oregon 1991.

Pinto Prades, José-Luis. Is the person trade-off a valid method for allocating health care resources? Health Economics 1997,6, 71-81.

Sintonen H. An approach to measuring and valuing health states. Soc Sci & Med 1981;15c:55-65.

Torrance GW. Measurement of health state utilities for economic appraisal. Journal of Health Economics 1986, 5, 1-30.

Ubel PA, Kamlet M, Scanlon D, Loewenstein G. Individual utilities are inconsistent with rationing choices: A partial explanation of why Oregon’s cost-effectiveness list failed. Medica Decision Making 1996,16,108-119.

Williams A. Ethics and efficiency in the provision of health care. In Bell JM, Mendus S (eds). Philosophy and Medical Welfare. Cambridge University Press 1988.

Williams A. Intergenerational equity: An exploration of the ‘fair innings’ argument. Health Economics 1997,6,117-132.










Table 1. Societal values for health improvements.

To level:

From level








1. None








2. Slight problem








3. Moderate








4. Considerable








5. Severe








6. Very severe








7. Completely disabled








8. Dead









Examples at levels 2-7:

2. Can move about anywhere, but has difficulties with walking more than 2 kms.

3. Can move about without difficulty at home, but has difficulties in stairs and outdoors.

4. Moves about without difficulty at home. Needs assistance in stairs and outdoors.

5. Can sit. Needs help to move about – both at home and outdoors.

6. To some degree bedridden. Can sit in a chair part of the day if helped up by others.

7. Permanently bedridden.



Table 2. Health state values encapsulating concerns for severity and realisation of potential.


Problem level


1. Healthy


2. Slight problem


3. Moderate


4. Considerable


5. Severe


6. Very severe


7. Completely disabled


8. Dead