Measurement of Short Term HealthEffects in Economic EvaluationsAnn M. Holmes

School of Public and Environmental Affairs, Indiana University-Purdue University at Indianapolis,Indianapolis, Indiana, USA

Summary Short term health effects can significantly impact health-related quality of life(HR-QOL). Appropriate healthcare priorities can be set only if they are based onhealth status measurements which are consistent with how people value both shortand long term health effects. This article discusses methods by which such healtheffects may be measured using health state utilities. The standard discountedquality-adjusted life-year model, in which the values of the various health statesare weighted by the time spent in each state, generally fails to capture the trueimpact of temporary ill health on HR-QOL. Instead, a scenario approach is recom-mended in which valuations are based on holistic descriptions of health stateswhich include all short and long term health effects experienced.

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The economic evaluation of health techno-logies, including pharmaceuticals, frequently re-quires the quantification of their impact on health-related quality of life (HR-QOL). To be useful insetting priorities, such measurements must assignhigher values to better health outcomes and, for thepurposes of cost-effectiveness analysis, largervalue differences to more important health im-provements. Health-state utilities have been prom-ulgated in such applications because of their rela-tionship with individual preferences for health andbecause, under certain assumptions, they generatemeasures on an appropriate interval scale.[1,2] Inthis paper, I discuss the debate surrounding howshort term health effects should be included in suchmeasures.

With few exceptions, the formal derivation ofhealth-state utilities and subsequent empirical ap-plications have been defined over health profileswhich are not only chronic but constant as well.Such constancy in health status is an abstraction at

odds with reality. A typical health profile can berepresented more accurately as a series of tempo-rary health states (or in a world with uncertainty, asa series of states and associated probabilities) thanas a single, constant health state. A temporaryhealth state differs from such a chronic state notonly in duration (shorter), but also the timing of itsoccurrence (either in the present or future), and dif-ferences from its adjacent states (better or worsehealth). For instance, many treatments for cancerinvolve negative short term adverse effects in thepresent to achieve better health in the future. Theshort term effects are hardly irrelevant, as evi-denced by the decisions of some cancer patients toforgo treatment. Thus, the impact of temporaryhealth effects on HR-QOL must be quantified ifhealthcare priorities are to be appropriately identi-fied.

There are 2 general approaches to measuringHR-QOL when health status is variable.[3] In theholistic or scenario approach,[4] valuations are

based on an assessment of the entire health pro-file.[5] For example, to evaluate the impact of che-motherapy in cancer treatment, the health-scenariodescription would include both the short term ef-fects of chemotherapy and the health state that re-sulted from treatment. Theoretically, values ob-tained by this method have the same measurementproperties as health-state utilities defined overchronic health states. Empirically, however, thisapproach presents 2 challenges. First, it requiresthe valuation of a large number of possible healthprofiles (if there are M possible health states thatmay be experienced during N distinct stages of life,the number of required valuations could be as highas MN). The assessment of probabilities in an un-certain world increases the number of valuations ina similar exponential fashion.

Second, inclusion of the various health statesexperienced in a health-profile description can cre-ate significant cognitive burdens for individualsasked to value such health profiles. The reliabilityof such valuations is likely to be compromised asthe number of attributes included in the scenarioexceeds the respondent’s ability to absorb addi-tional information. Evidence from psychology sug-gests that the maximum number of attributes is inthe range of 5 to 9,[6] although recent findings sug-gest the number could be lower in health evalua-tions.[7] The likely result is an imprecise estimateof the theoretically correct utility value for a par-ticular health profile.

An alternative approach is to construct the uti-lity value for a health profile from some functionof the values for its component parts.[8] Compo-nent parts may be valued as chronic states (whichlast for a specified duration and conclude withdeath) or as temporary states which are embeddedin a scenario in which the rest of life is arbitrarilyassumed to be spent in perfect health.[1] Such adecomposition approach underlies the quality-adjusted life-year (QALY) model, where the valuesof the various health states are weighted by the timespent in each state, and in economic evaluationsbased on Markov models, where such values arealso weighted by the probability of entering a given

health state. While more feasible than the holisticapproach, the QALY approach fails to meet themeasurement criteria outlined above except undervery strict assumptions about the nature of people’spreferences for health. QALYs may not assignhigher values to better health states unless additiveutility independence holds.[2,9] This condition re-quires that values for a health state in one periodcannot depend on the health state experienced inany other period: the sequencing of health states isirrelevant.[10] In addition to utility independence,preferences must also exhibit constant proportionaltrade-off (i.e. valuations are invariant to the dura-tion of the health state) and risk neutrality.[2,9] Evi-dence as to whether preferences are consistent withthese restrictions is mixed. While direct empiricaltests of preferences have sometimes supportedthese restrictions,[9,11] considerable evidence refu-ting constant proportional trade-off exists, particu-larly for states of very short duration.[12-15]

Clearly, neither the holistic nor the standardQALY approach is completely satisfactory. In res-ponse, a variety of hybrid approaches have beendeveloped which attempt to incorporate more in-formation about preferences without valuing everypossible permutation of health. One proposal is thehealthy years equivalent (HYE), which was expli-citly designed to value health profiles.[16] Thus,HYE values achieve the desirable measurementproperties without imposing as many restrictionson how people value health as constructed QALYvalues. Indeed, HYE values and holistic utilitiesappear to be theoretically equivalent.[17] Whilesome investigators find HYE values to be more in-tuitive than holistic utilities,[18] the HYE approachmay be less feasible than the holistic approach be-cause it requires valuations be made in 2 stepsrather than directly.

Alternative proposals have modified the combi-nation rule for QALYs to accommodate duration,timing and sequencing effects of temporary healthstates. Typically, this involves using discountedQALYs.[19] Such geometric discounting models areattractive from a policy standpoint because theygenerate decisions which exhibit intertemporal

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consistency (decisions about which policies arebetter are not reversed after they are implemented).

The empirical challenge is to find the appropri-ate discount rate. Because this rate is influencednot only by the timing of health events, but alsotheir duration and sequencing,[20,21] identifying thecorrect rate for any particular situation is apt to beextremely difficult. The Panel on Cost-Effectivenessin Health and Medicine in the US[19] recommendedusing a rate of 3% to discount both future costs andhealth benefits. Because the values for severehealth states tend to fall with increased duration,[12]

such simple discounting rules may overvalue thesestates. Also, the rate may not capture possible se-quencing effects. While there is little empirical evi-dence as to the effects of sequencing per se, otherframing effects have been noted in previous utilityanalyses,[22] with losses (decrements) valued dif-ferently from gains. Finally, since duration is in-herently part of any health state, QALY values in-clude a time preference component. DiscountingQALY values at annualised rates may over- or un-derstate true time preferences depending on theoriginal duration used in the QALY valuation.[23]

Temporary health effects can have significantimpacts on HR-QOL, impacts that can influence apatient’s preferred choice of treatment and there-fore should influence the setting of policy priori-ties. Incorporating such effects into utility evalua-tions poses significant challenges and is likely toinvolve trade-offs between the reliability and va-lidity of the values obtained. Unfortunately, thereis little empirical evidence as to the degree of com-promise associated with any particular approach tomeasurement.

The main concern with the holistic approach isone of cognitive burden. Additional research isneeded to determine how reliability is affected asthe number of attributes in the health scenario isincreased. A paucity of evidence exists even in thestandard case where the scenario consists of achronic and constant health state,[7] much less theadditional dynamic complexities of evolvinghealth profiles, or the probabilistic dimensions ofprognosis.

Empirical evidence suggests that people do notvalue health states in a way that would support thevalidity of the QALY approach to measuring HR-QOL. Less empirical work exists as to the size ofthe impact such violations have on utility values.Two notable exceptions[5,24] have compared theholistic and QALY approaches directly, findingthat the QALY approach consistently overvaluedhealth profiles associated with mastectomy andprenatal testing. Of particular concern was the factthat the annualised differences between holistichealth state utilities and undiscounted QALYs ex-ceeded 0.10,[5] a value generally deemed to be cli-nically significant.[25] Adjustments for discountingfailed to account for the observed differences. Al-though reasonable approximations were obtainedusing regression methods in 1 study,[24] such weightshave no underlying conceptual basis to suggest ap-propriate combination rules to use in other appli-cations.

Mounting empirical evidence suggests dis-counted QALY models, which have been recentlyadvocated as the standard in economic evalua-tion[19] and which underlie Markovian decisionmodels, cannot accurately represent preferencesfor health profiles with significant short termhealth effects. While holistic methods are more dif-ficult to implement, they do ensure that values areassigned to health profiles in a way which is con-sistent with how people value health. If economicand decision analyses are to identify appropriatehealthcare priorities, they must be based on suchoutcome assessments.

Acknowledgements

The author thanks 2 anonymous referees for their helpfulcomments on an earlier version of this paper.

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Correspondence and reprints: Dr Ann Holmes, School ofPublic and Environmental Affairs, Indiana University-Purdue University at Indianapolis, 801 W. Michigan Street– Room 4070, Indianapolis, IN 46202-5152, USA.E-mail: aholmes@speanet.iupui.edu

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