Recent advances in understanding links between genes

and the susceptibility to particular diseases have considerably increased

the scope for predictive diagnosis. Methods. We analyse how the introduc-

tion of predictive diagnosis affects patients�decisions to undergo medical

screenings relying on a �rational choice�model. Findings. We show that

predictive diagnosis can increase the number of fatalities from a deadly

disease. Interpretation. Our result shows the necessity of careful further

analysis and debate about the pros and cons of predictive diagnosis and

the publication of medical research in general

Huck, S.

Konrad, K.A.

English

UCL Discovery

Information can killSte⁄en Huck￿University College London & ELSEKai A. KonradSocial Science Research Center Berlin (WZB) and Free University of BerlinApril 28, 2004AbstractBackground. Recent advances in understanding links between genesand the susceptibility to particular diseases have considerably increasedthe scope for predictive diagnosis. Methods. We analyse how the introduc-tion of predictive diagnosis a⁄ects patients￿decisions to undergo medicalscreenings relying on a ￿rational choice￿model. Findings. We show thatpredictive diagnosis can increase the number of fatalities from a deadlydisease. Interpretation. Our result shows the necessity of careful furtheranalysis and debate about the pros and cons of predictive diagnosis andthe publication of medical research in general.(1787 words.)￿Department of Economics & ELSE, University College London, Gower Street, LondonWC1E 6BT, UK; email: s.huck@ucl.ac.uk, phone +44 20 7679 5895, fax +44 20 7916 2774.Financial support from the Leverhulme Trust and the ESRC via ELSE is gratefully acknowl-edged.11 IntroductionRecent advances in understanding links between genes and the susceptibilityto particular diseases have made it possible to warn certain parts of the pop-ulation about their increased risk of contracting diseases. Prominent examplesfor predictive genetic diagnosis are Huntington￿ s disease, morbus Crohn, or aninherited predisposition to breast cancer. For a survey see, e.g., Burke [1]. Suchinformation should make a carrier of traits that are linked to a disease morelikely to take precautions, for example, to undergo regular medical screeningthat enable early treatment for the disease if diagnosed. Intuitively, one wouldthink that because of this, such information should lower the number of peoplewho develop and die from a disease and, thus, increase average health in thepopulation. In this note we show that this need not necessarily be true.In fact, information about a link between a certain trait (like sex of thepatient) and a particular disease can increase the number of people developingand dying from a disease. The reason for this counterintuitive ￿nding is thatthe bad news for some parts of the population (who learn that they are morelikely to develop a disease) is good news for the remaining part of the population(who, by the law of average, ￿nd themselves less likely to develop the disease).Accordingly, learning about how certain traits are linked to certain diseases hasa di⁄erential impact on the population: Some parts of the population will gomore frequently to medical screening, others go less frequently. Here we showthat the latter e⁄ect may outweigh the former.We obtain this result by studying a rational choice framework where patientsdecide in a fully rational manner whether or not to undergo medical screening.They weigh the costs and bene￿ts of undergoing the screening and decide to go a2screening if and only if the bene￿ts outweigh the costs [2, 3]. Rational economicdecision making in this fashion can operate on a concious or a subconcious level[3].Earlier, Marteau and Lerman [4] have warned of adverse e⁄ects similar tothose analysed here by invoking psychological arguments. They write (p. 1058):￿It will also be important to ensure that people who are found not to be atincreased risk do not develop a false sense of reassurance, feeling invulnerableto the adverse e⁄ects of their risky behaviour. Such people need to understandthat they have a residual (albeit lower) risk of developing the condition and tobe encouraged to engage in risk reducing behaviours relevant for the generalpublic.￿ In some sense, we show that predictive genetic diagnosis and, moregenerally, any information about certain traits and diseases can have adversee⁄ects even if people do not develop a ￿a false sense of reassurance￿ . Peoplemight process the provided information perfectly well and this alone might causeadditional deaths. Psychological biases as suggested by Marteu and Lermanmight then, of course, aggravate the problem further.We think our analysis raises important questions regarding both, genetictesting and the publication of medical research. Widespread publication ofcertain ￿ndings about the risk of contracting diseases can cause higher incidentsof the disease and, if the disease is dangerous, more deaths.32 AnalysisConsider a potentially deadly disease that we shall simply call D. To keep thingsas simple and illustrative as possible we make some assumptions about how Dcan be diagnosed and treated. D can be diagnosed before it breaks out. Thediagnosis is perfect and if a patient is diagnosed she can be treated. However,there is no treatment once D has broken out, and the patient will die. To bemore speci￿c, consider an example with a population of 100 individuals, eachdepicted as one cell in Figure 1. Only ten percent of the population will developthe disease D, the others will not. In the ￿gure, these are the red and white cells,respectively. Individuals in the population do not know whether they belong tothe group of red cells or white cells; hence, the average risk of contracting thedisease in this case is 10 percent for each individual.Each individual in this population has to make a decision about whether toattend regular health checks/screenings. To describe this decision problem weneed to know how the patient evaluates di⁄erent health outcomes. Followingthe standard approach in health economics, we employ the notion that witheach outcome a patient associates a ￿utility￿ level [3]. For simplicity, let usassume that there are only three possible health outcomes: (1) A patient doesnot contract the disease. This is the best outcome associated with the highestutitility level, say, 1. (2) A patient develops D before it was diagnosed anddies. This is the worst outcome, with utility level 0. (3) A patient with D isdiagnosed before the disease breaks out and treated accordingly. This is theintermediate outcome associated with a utility level b between the ￿rst two. Ifthere is a good treatment for D (almost) restoring perfect health, b will be closeto 1. If the treatment has many side e⁄ects and only partially restores health,4b will be much closer to zero.Individuals must decide whether to undergo regular screening, which imposessome costs upon the patient. These costs can be material (for example, fees andtransport) but also psychological (for example, embarassment and pain) [2], andmay also di⁄er between patients depending on where they live and what cost iscovered by their health insurance. To go back to our illustrative example, let usassume that there are 60 individuals who have low cost c of screening, and 40individuals who have high cost C. These costs are measured in the same unitsas the ￿utility￿from the various health outcomes above. The risk of contractingthe disease is independent of the individual costs for undergoing screening. InFigure 1, the individuals to the left of the bold dividing line have low cost, andthe ones to the right have high cost.Individuals will undergo screening if their cost is lower than the expectedbene￿t of screening. There is no bene￿t of screening for those who do not con-tract the disease. For those who would contract the disease, the bene￿t equals b,i.e., the di⁄erence between their utility from being diagnosed and treated andtheir utility from contracting the disease and dying. To an individual who doesnot know whether she is one of the red or one of the white cells, this bene￿toccurs with a 10 percent probability. Hence, the expected bene￿t is b10. Indi-viduals will now compare the expected bene￿t from screening with their costs.Now consider the following case where the low costs c are smaller than b10, andthe large costs C are greater than b10. In this case all individuals with low costsundergo the screening, and all individuals with high cost do not. Accordingly,the total number of deaths will be equal to 4, represented by the four red cellsamong the high cost individuals in Figure 1.5Next, let us examine what happens in this example where there is somesort of predictive medicine. Let some 10 percent of the population carry some(genetic) trait that increases the likelihood of contracting the disease by a factorof 5, i.e., from 10 percent to 50 percent, and let us assume that everybody knowstheir trait. Individuals will know their trait either if it is easily observable like asthe case of sex or if the whole population is subjected to the predictive genetictesting, say, at birth. In any case, in our example this means that there arenow 10 individuals who carry the trait and have a 50% chance of contractingD. These individuals are represented as the cells labelled ￿ T￿ (for ￿trait￿ ) inFigure 2. The other 90 individuals are captured by the 90 cells labelled ￿ 0￿ .The trait is unrelated to the costs of undergoing a health check. Hence, amongthe 10 individuals who carry the trait there 6 individuals with low costs and 4individuals with high costs, as depicted in Figure 2.Consider now how the genetic information changes decision behavior. Thegroup of carriers of the dangerous gene is likely to undergo screening (unless Cis very high). Hence, all 5 lives of individuals in this group who would contractthe disease will be saved. On the other hand, the individuals with high costwho do not carry the dangerous gene will not screen. Hence, the two (red)individuals in this group who are at risk will contract the disease and will die.These are shown by the two red cells in the bottom right corner of Figure 2. Insome sense, these two individuals cannot be saved: They die with and withoutthe predictive test.Finally, consider the group of individuals who have low cost of screening,but have learned that they do not carry the dangerous gene (all cells labelled ￿ 0￿to the left of the dividing line). They know now that they are at lower risk. In6fact, they have the bene￿t b of screening only in 5 of 90 cases. Hence, they willnot screen unless their costs are very low, c ￿ b=18, (compared to c ￿ b=10 inthe case without predictive diagnostics). Therefore, if c is in the range betweenb=10 and b=18, the genetic test will change their behavior from ￿ screen￿to ￿ notscreen￿ , and this will cause three more deaths. These are shown by the red cellsin the bottom left corner of Figure 2.On balance, we ￿nd that with genetic testing there will be 5 deaths whilewe only 4 deaths occured without genetic testing. Note that the individualsact fully rationally here; i.e., they choose actions that maximize their ex-anteexpected utility. But still, the number of deaths increases.73 ConclusionThe widespread availability of predictive genetic diagnosis and information aboutgenetic susceptibility to diseases will induce major changes of individuals￿will-ingness to undergo medical screening. We show that the total number of victimsof a curable disease can increase as a consequence of predictive genetic diagnosis.In a next step we plan to analyse the problem in a more general frameworkthat allows us to make inferences about when and where the release of infor-mation about links between traits and diseases is perticularly dangerous. Ourresults so far indicate that the negative e⁄ect of predictive diagnosis is partic-ularly powerful if the number of patients with comparatively low psychologicaland/or monetary costs for undergoing a health check is large. This, of course,means that the problem that we sketch is more pronounced in populations wheremost individuals have good medical insurance.Both authors contributed to the idea of this paper, the analysis, and thewrite-up.We have no con￿icts of interest to declare..8References[1] Burke, W, 2002, Genetic testing, New England Journal of Medicine, 347,1867-1875.[2] Mansley, EC and MT McKenna, 2001, Importance of perspective in eco-nomic analyses of cancer screening decisions, Lancet, 358, 1169-73.[3] Meltzer, MI, 2001, Introduction to health economics for physicians, Lancet,358, 993-98.[4] Marteau, TM. and C Lerman, 2001, Genetic risk and behavioral change,British Medical Journal, 322, 1056-1059.910

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